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• The classification of brain tumors (BTs) is an expensive intricate challenge in the section of clinical image analysis. Radiologists were able to reliably detect tumors using machine learning (ML) algorithms without the need for extensive surgery. • The efficacy of this paper is examined using classification accuracy. • The suggested model demonstrated its advantage over the currently used approaches for classifying BTs from MR images. The classification of brain tumors (BTs) is an expensive intricate challenge in the section of clinical image analysis. Radiologists were able to reliably detect tumors using machine learning (ML) algorithms without the need for extensive surgery. Hand-crafted features are necessary for the conventional ML classifiers, which take a lot of effort. Deep Learning (DL), on the other hand, has gained popularity recently for identification and categorization-based functionalities because of its effective feature extraction capabilities. Creating the most precise DL architecture for classifying BTs is a huge task. These challenges inspired us to develop a cutting-edge and highly accurate framework based on DL and computational methods that evolve to effectively create the hybrid framework automatically for categorizing three distinct types of brain cancers on a vast library of MR images. We present a unique EBT Deep Net DL model that uses a substantial convolutional neural network (CNN) technique for the classification of three distinct forms of brain cancers: gliomas, tumors in brain meningeal tissue, and tumors in pituitary glands. Pretrained deep CNN models like ResNet50V2, ResNet152, DenseNet201, MobileNetV3, and VGG19 were used to extract deep features. The efficacy of this paper is examined using classification accuracy. In comparison to the most recent classification findings from ResNet50V2, ResNet152, DenseNet201, MobileNetV3, and VGG19, the proposed methodology had the greatest accuracy. The models' respective accuracy on a classification test is as follows: VGG19 scored 97.46 %, ResNet152 scored 98 %, DenseNet201 scored 95.44 %, ResNet50V2 scored 90.04 %, MobileNetV3 scored 97.15 %, and the ensemble model outperformed all of them with an accuracy of 98.8 %. The suggested model demonstrated its advantage over the currently used approaches for classifying BTs from MR images. [ABSTRACT FROM AUTHOR]

• This paper offers a technique for the identification of tumour regions. • This study isolates tumorous regions using pixel-wise segmentation. • The proposed method segments tumours of various sizes with an average Dice score of 0.82. Before surgery, medical segmentation is carried out to ascertain the limits of regions of interest (ROI). Critical information may be gathered during the planning phase of the operation if it is permitted that the growth, structure, and behaviour of the ROI be studied. This will increase the probability that the operation will be successful. Most of the time, segmentations are carried out either manually or via machine learning algorithms based on hand annotations. The difference between pure background information and an approximation of such information would affect the performance of brain tumour detection algorithms. This paper offers a technique for the identification of tumour regions that is based on kernel background filtration. The proposed approach presented has a more precise estimation of background information as its primary objective. It is composed of four stages. The first stage includes a probabilistic method based on image denoising. The second stage includes extracting the pure background pixels using a kernel-based technique. The third stage estimates the background covariance matrix using the pixels taken from the pure background pixels. In the fourth phase, we perform segmentation using DCNN. The segmentation procedure begins with training a deep convolutional neural network (DCNN) to recreate damaged brain regions. Window-based methods identify pixels with the largest reconstruction loss. This study isolates tumorous regions using pixel-wise segmentation. The proposed method segments tumours of various sizes with an average Dice score of 0.82. [ABSTRACT FROM AUTHOR]

Automatic detection of Diabetic Retinopathy (DR) is critically important, as it is the primary reason of irreversible loss of vision in the economically active populations in the developed countries. Early detection of the onset of Diabetic Retinopathy can greatly benefit clinical treatment; although several different feature extraction methods have been proposed, the task of retinal image classification remains tedious even for trained clinicians. This paper emphasizes on Diabetic Retinopathy detection as well as the analysis of the different stages of DR, performed on fundus images using Deep Learning algorithms. Fundus images of the patient were provided as input to the developed model evaluated using the real-time dataset of the hospital. The proposed ResNet-18 architecture with swish function has achieved an accuracy of 93.51%, sensitivity of 93.42%, precision of 93.77% and F1-score of 93.59%. The paper concludes with a comparative study of Simple CNN, VGGNet-16, MobileNet-V2 and ResNet architectures and other state-of-art approaches, which highlights ResNet-18 with Swish as the most effective deep learning classifier model for DR detection. • Deep learning outperforms traditional screening for Diabetic Retinopathy, reducing ophthalmologists' workload. • ResNet18's residual connections mitigate the vanishing gradient problem and enable effective retinal image pattern learning. • Swish activation in ResNet18 enhances Diabetic Retinopathy detection with improved accuracy and faster training convergence. [ABSTRACT FROM AUTHOR]

• We proposed a novel method for noise reduction of multichannel heart sound signals, which was based on FastICA algorithm and CNN model. • The method performs classification and consistency analysis of the independent components to extract reliable independent components of heart sounds. • The method improves the SNRs of each channel signal up to 20 dB, which is superior to the Wiener filtering and singular spectrum analysis (SSA). Various noises could contaminate heart sounds and yield inaccurate diagnosis. This paper proposed a framework of heart sound noise reduction using multi-channel structure. Multi-channel heart sound (MCHS) signals were preprocessed, and then decomposed into independent components (ICs) using FastICA algorithm. The ICs were evaluated for consistency using correlation analysis, and reliable ICs were identified. The reliable ICs were classified based on a convolutional neural network (CNN) into two groups: one is of ICs for heart sounds and the other is of ICs for noise, respectively. Consequently, the MCHS signals were reconstructed from the ICs group of heart sounds. To validate the proposed framework, the MCHS signals of 15 subjects were acquired and mixed with breath sounds under different signal-to-noise ratios (SNRs). the simulation results showed that the framework had effective noise reduction performances in suppressing the breath sounds. The SNR of the denoised MCHS could be improved to 20 dB. the proposed framework was better than traditional methods, such as Wiener filter and singular spectrum analysis (SSA). The practical applications proved that the framework is possible to reconstruct the heart sound signals in low signal quality situation. [ABSTRACT FROM AUTHOR]

Recent advancements in Deep Learning models hold the potential to revolutionize the automated analysis of EEG data for early and accurate diagnosis of epileptic seizures. This paper introduces an interpretable hybrid model, integrating Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks, with a primary emphasis on two-class epileptic seizure classification. Extensive evaluations across diverse datasets establish the model's resilience and effectiveness. Notably, in the CHB-MIT dataset, the model achieves an average validation accuracy of 92.8 %, an average area under the curve of 93 %, an average specificity of 90.3 %, an average sensitivity of 95 %, an F1-score of 94 %, and an MCC of 88.2 %. In the Siena dataset, an average validation accuracy of 92.7 %, an average area under the curve of 93 %, an average specificity of 84 %, an average sensitivity of 91 %, an F1-score of 92.5 %, and an MCC of 85 % are maintained. In the Helsinki dataset, the model attains an average validation accuracy of 86.4 %, accompanied by an average area under the curve of 86 %, an average specificity of 84 %, a sensitivity of 88 %, an F1-score of 87.8 %, and an MCC of 75.3 %. Furthermore, the proposed model provides a post-hoc explainer utilizing the Shapley Additive Explanations (SHAP) method, specifically the SHAP Gradient Explainer that interprets the predictive model by providing two forms of explanation: (i) Event-wise explanations, elucidating why particular EEG data segments are classified as seizures or non-seizure events, and (ii) Patient-wise explanations that precisely pinpoint the brain lobe and hemisphere responsible for the seizure's origin. The explainer's efficacy is meticulously assessed using ground truth data, yielding localization and lateralization accuracy scores of 85.43 % for the CHB-MIT dataset, 86 % for the Siena dataset, and 79.4 % for the Helsinki dataset. This research contributes to the advancement of the responsible and trustworthy use of Artificial Intelligence in seizure vs. non-seizure EEG classification and interpretation, delivering both precise classification and in-depth explanations. [ABSTRACT FROM AUTHOR]

• Interpretable network design: NEFGN incorporates physical models to make the denoising network more interpretable. By grounding the network design in physical principles, it becomes easier to understand the underlying processes of noise reduction. • The NEFGN method proposed in this paper combines the advantages of traditional denoising and deep learning denoising to provide better interpretability and stronger noise reduction ability. • Construction of noise optimization model stage: By learning the parameters of the adaptive weighted total variation regularization model, NEFGN can approximate the noise level in NDCT images, so as to remove the noise in LDCT images more accurately. In addition, the study introduced noise level estimation maps as a means to accurately control the balance between noise reduction and detail retention for optimal visualization and image quality. • Construction of Gaussian curvature edge detection model stage: NEFGN uses Gaussian curvature guidance to construct an edge detection network to directly predict clear edges from LDCT images. This helps to preserve important edge details and avoid image blurring or information loss. And an edge detector for making edge labels is proposed, which can better deal with the noise reduction task of low-dose CT images. • Comprehensive experimental evaluation: The proposed method was evaluated using the "NIH-AAPM-Mayo Clinic Low Dose CT Grand Challenge" dataset, piglet dataset, and clinical dataset. The experimental results demonstrate that NEFGN effectively enhances the diagnostic performance of CT images by restoring the LDCT image structure with constrained image details. • Superior performance: NEFGN outperforms other denoising algorithms in terms of both quantitative measures and visual quality. This makes it a promising candidate for clinical applications, where accurate interpretation of CT images is crucial for diagnosis and treatment planning. The denoising task of low-dose CT images is a highly complex and uncertain inverse problem. Previous studies have primarily relied on convolutional neural network to reduce noise by learning the mapping from LDCT images to normal dose CT images. However, simply increasing the network depth alone is not an optimal choice due to the limited performance improvement and significant computational cost. In contrast, integrating prior knowledge of images with a model to assist in image reconstruction is a more efficient approach. This study proposes a new framework for denoising LDCT images, named Noise-Optimized Edge Feature Guided Network (NEFGN). The task of NEFGN is to integrate the noise optimization model of adaptive weighted total variation expansion, the edge detection model guided by Gaussian curvature, and image reconstruction into an end-to-end CNN framework. In order to achieve this goal, the noise optimization model is first constructed by learning the parameters in the adaptive weighted total variation regularization model to approximate the noise level in the NDCT image. The edge detection network is constructed using Gaussian curvature, predicting clear edges directly from the noise image. Finally, under the guidance of the noise optimization model and the edge detail model, NEFGN is more capable of suppressing artifact noise, demonstrating good accuracy and robustness, and can restore finer details. Numerous experimental studies demonstrate that the NEFGN denoising framework effectively restores the structure of LDCT images with limited image details and outperforms other methods in terms of performance. [ABSTRACT FROM AUTHOR]

Segmentation of medical images is a critical step to distinguish different regions, especially tumor. The presence of severe intensity inhomogeneity in Magnetic Resonance Imaging (MRI) and Mammogram images makes it difficult to assess and detect the boundaries of tumor. Tumor detection in medical images measures and identifies the structure of the tumor. This challenging task of extracting tumor i.e., region of interest is achieved by proposing a novel fuzzy selective model. The proposed model is mainly based on selective segmentation to detect the tumor in medical images. This model is based on the local Gaussian distribution as a fitting term with fuzzy logic to handle uncertainty and irregular borders in medical images. Due to the usage of fuzzy membership functions, it avoids local minima and converges to the tumor boundary rapidly and efficiently. In addition, to obtain more information of a local intensity, the idea of multi-scale modeling is incorporated to achieve significant results. The proposed segmentation model is applied to diverse MRI and Mammogram datasets containing both the intensity inhomogeneity and noisy images, this data is collected from local hospitals and the ground truths are created by the experts. The extensive simulation results show that the proposed segmentation method significantly outperforms existing state-of-the-art techniques using both the qualitative and quantitative assessment. Loss functions play very important role in CNN architectures, and is well studied research area. In the second part of the paper, we will be introducing a new loss function, which is introduced in the first part of the paper. Deep active contour architectures based Chan-Vese loss function works well in images with homogeneity and may not work well in images with intensity inhomogeneity. We proposed an architecture, whose loss function will be based on this new proposed model. The proposed model is used as a loss function in a newly introduced CNN architecture, which has produced improved results. The results of the new architecture are compared with other existing methods used for segmentation of medical images. • A new model based on multi-scale image data is proposed for detection of tumor. • The model is further improved by using local Gaussian distribution combined with fuzzy set framework. • A new CNN based architecture based on Res-Net50 and DeepLab v3+ integrated with the new loss function is proposed. • Testing is done on local collected datasets. [ABSTRACT FROM AUTHOR]

Segmentation of medical images is a critical step to distinguish different regions, especially tumor. The presence of severe intensity inhomogeneity in Magnetic Resonance Imaging (MRI) and Mammogram images makes it difficult to assess and detect the boundaries of tumor. Tumor detection in medical images measures and identifies the structure of the tumor. This challenging task of extracting tumor i.e., region of interest is achieved by proposing a novel fuzzy selective model. The proposed model is mainly based on selective segmentation to detect the tumor in medical images. This model is based on the local Gaussian distribution as a fitting term with fuzzy logic to handle uncertainty and irregular borders in medical images. Due to the usage of fuzzy membership functions, it avoids local minima and converges to the tumor boundary rapidly and efficiently. In addition, to obtain more information of a local intensity, the idea of multi-scale modeling is incorporated to achieve significant results. The proposed segmentation model is applied to diverse MRI and Mammogram datasets containing both the intensity inhomogeneity and noisy images, this data is collected from local hospitals and the ground truths are created by the experts. The extensive simulation results show that the proposed segmentation method significantly outperforms existing state-of-the-art techniques using both the qualitative and quantitative assessment. Loss functions play very important role in CNN architectures, and is well studied research area. In the second part of the paper, we will be introducing a new loss function, which is introduced in the first part of the paper. Deep active contour architectures based Chan-Vese loss function works well in images with homogeneity and may not work well in images with intensity inhomogeneity. We proposed an architecture, whose loss function will be based on this new proposed model. The proposed model is used as a loss function in a newly introduced CNN architecture, which has produced improved results. The results of the new architecture are compared with other existing methods used for segmentation of medical images. • A new model based on multi-scale image data is proposed for detection of tumor. • The model is further improved by using local Gaussian distribution combined with fuzzy set framework. • A new CNN based architecture based on Res-Net50 and DeepLab v3+ integrated with the new loss function is proposed. • Testing is done on local collected datasets. [ABSTRACT FROM AUTHOR]

Non-Small Cell Lung Cancer is the most common type of lung cancer, accounting for more than 80% of all cases. The analysis of histopathological images is the appropriate way to detect lung cancer in its initial stages. However, due to the heterogeneous patterns of molecular structures and the high magnification factor, the classification of whole slide images is difficult. Furthermore, these large-pixel images cannot be considered directly due to the high computational requirements. Several deep-learning models have been developed for the classification of histopathological images, but only in the default RGB format. Existing models are complex and rely on pathologist annotations which increases the computational cost and time. The present paper proposes a deep-learning framework, a Color-based Dilated Convolutional Neural Network (CD-CNN) for the multi-class classification of patch-based whole slide images of lung cancer. The proposed CD-CNN framework includes data preparation, color space transformation, and the classification stages. It thoroughly examines the impact of five color spaces on classification performance. The proposed model was also compared to three pre-trained models and the Convolutional Neural Network model on three different datasets namely: The Cancer Genome Atlas dataset, the Clinical Proteomic Tumor Analysis Consortium cohorts, and the LC25000 dataset. The analysis of all the models reveals that the HSV color space outperforms the other transformations for all of the datasets tested. The results depict that the proposed CD-CNN achieved the best classification results in HSV color space for all three datasets i.e. accuracy of 0.97–0.99, precision, recall, and F1 score 0.97–0.98 and AUC value of 0.970–0.984. In addition, the highest value of the kappa score 0.986 has been achieved by the proposed model. These findings demonstrate the efficacy of the proposed method and show an improvement in diagnostic systems. • The analysis of Whole Slide Images (WSIs) for the classification of lung cancer is difficult due to its heterogeneous structure. • The present paper proposes a color-based deep-learning framework for the classification of lung cancer-type WSIs. • The proposed framework thoroughly investigates the impact of five color spaces: RGB, YCbCr, LAB, HSV, and XYZ on the classification performance. • Three different lung cancer datasets have been used to validate and test the performance of the proposed model. Thorough investigation and comparative analysis indicate the efficacy of the proposed framework over other state-of-the-art methods. [ABSTRACT FROM AUTHOR]

The existence of fundus diseases not only endangers people's vision, but also brings serious economic burden to the society. Fundus images are an objective and standard basis for the diagnosis of fundus diseases. With the continuous advancement of computer science, deep learning methods dominated by convolutional neural networks (CNN) have been widely used in fundus image classification. However, the current CNN-based fundus image classification research still has a lot of room for improvement: CNN cannot effectively avoid the interference of repeated background information and has limited ability to model the whole world. In response to the above findings, this paper proposes the CNN-Trans model. The CNN-Trans model is a parallel dual-branch network, which is the two branches of CNN-LSTM and Vision Transform (ViT). The CNN-LSTM branch uses Xception after transfer learning. As the original feature extractor, LSTM is responsible for dealing with the gradient disappearance problem in neural network iterations before the classification head, and then introduces a new type of lightweight attention mechanism between Xception and LSTM: Coordinate Attention, so as to emphasize the key information related to classification and suppress the less useful repeated background information; while the self-attention mechanism in the ViT branch is not limited by local interactions, it can establish long-distance dependence on the target and extract global features. Finally, the concatenation (Concat) operation is used to fuse the features of the two branches. The local features extracted by the CNN-LSTM branch and the global features extracted by the ViT branch form complementary advantages. After feature fusion, more comprehensive image feature information is sent to the to the classification layer. Finally, after a large number of experimental tests and comparisons, the results show that: the CNN-Trans model achieved an accuracy of 80.68% on the fundus image classification task, and the CNN-Trans model has a classification that is comparable to the state-of-the-art methods. performance.. [ABSTRACT FROM AUTHOR]

Fall detection is an emerging problem in the field of elderly care and health monitoring. Falls can cause severe injuries and even death among the elderly, and prompt detection can significantly reduce the risk of further damage. Deep learning plays a pivotal role in fall detection by enabling the recognition of complex patterns and the analysis of spatial and temporal data, leading to accurate and efficient identification of fall events. However, some recent deep learning models still face challenges and limitations, particularly in terms of interpretability and explainability. In this paper, CABMNet has been proposed, an adaptive two-stage deep learning network that optimizes spatial features through convolutional neural networks (CNNs) and convolutional block attention module (CBAM) in the initial stage, followed by bidirectional long short-term memory (Bi-LSTMs) enhanced with multi-head attention (MHA) for temporal analysis in the subsequent stage. The incorporation of attention in the proposed work addresses issues including feature discrimination, temporal analysis, and model interpretability, leading to improved overall performance and efficiency of fall detection. Additionally, a Kalman filter is integrated to enhance noise reduction in the preprocessing stage. Evaluation on diverse five datasets indicates that CABMNet outperforms existing state-of-the-art models, achieving accuracy rates of 99.27% for KFall, 99.85% for MobiAct, 99.55% for SmartFall, 98.36% for URFall, and 98.52% for MobiFall. The proposed CABMNet demonstrates substantial applicability in healthcare, specifically in the domains of elderly care, health monitoring, and fall detection, contributing to enhanced well-being and safety for elderly people. • A two-stage model, CABMNet, integrates CNNs with CBAM and Bi-LSTMs with MHA. • CBAM with CNNs optimizes spatial feature extraction and computational efficiency. • BiLSTM uses MHA for precise recognition of crucial time steps and complex patterns. • The Kalman filter improves noise reduction and state estimation in preprocessing. • CABMNet is evaluated on KFall, MobiAct, SmartFall, URFall, and MobiFall datasets. [ABSTRACT FROM AUTHOR]

The estimation of respiratory rate (RR) is an extremely crucial step in the monitoring of a patient's health care. Nowadays, non-contact physiological signal measurement based on face videos has great potential for various applications. However, current RR estimation methods still face challenges in solving remote RR estimation tasks because the color change of facial skin is very subtle and the quasi-periodicity of the remote photoplethysmography(rPPG) requires the long-range temporal modeling. In this paper, we propose a dual-branch network that consists of an attention based convolutional neural network (CNN) and Transformer network (ACTNet), which can fully utilize local features and global information. In ACTNet, the CNN branch captures subtle color changes between facial image frames and introduces local attention to further highlight local features, and the Transformer branch models the long-term temporal relationships between video frame sequences to obtain global information. Then we fuse local features and global information into the network using the feature coupling unit. Comprehensive experimental results on COHFACE and DEAP datasets show that our method achieves state-of-the-art performance. • We propose an ACTNet consisting of attention based CNN and Transformer for RR estimation from facial videos, and it takes into account both global contextual information as well as local features. • Attention based CNN is used to enhance the local feature extraction ability which allows ACTNet to capture the subtle color changes from facial videos. • Experimental evaluation is performed on COHFACE and DEAP datasets, and ACTNet achieves state-of-the-art performance including Mean Absolute Error, Root Mean Square Error, Pearson Correlation Coefficient and generalization ability. [ABSTRACT FROM AUTHOR]

Lymphoma is a malignant tumor originating from the lymphohematopoietic system. At present, pathological evaluation is one of the important methods to diagnose malignant lymphoma. In clinical practice, the diagnosis of lymphoma, especially in newly diagnosed patients, depends mainly on histopathological examination of the lesion. The type of lymphoma is determined by repeatedly comparing hematoxylin-eosin (H&E) whole slide images (WSIs) and immunohistochemical WSIs under a microscope. It is a repetitive, tedious, and time-consuming process. Therefore, it is extremely important to establish a highly accurate and standardized lymphoma diagnosis algorithm. In this paper, we developed an innovative deep-learning framework based on multi-model fusion, which only uses the H&E slides, with special attention to gastric Mucosa-associated lymphoid tissue (MALT) lymphoma diagnosis. The proposed framework can evaluate and improve the auxiliary ability of the convolutional neural network (CNN) in clinical practice for the diagnosis of gastric MALT lymphoma. The proposed method achieved an accuracy of 98.53% using image patches and an accuracy of 94.96% on 258 WSIs. These results show the high accuracy in the diagnosis of MALT lymphoma and its potential use in clinical practice. In addition, we also estimated the 95% confidence interval of WSIs prediction values. The result shows that the proposed framework has a high degree of differentiation in the interpretation between gastric MALT lymphoma and normal pathological tissues. [ABSTRACT FROM AUTHOR]

• Automatic croup and pertussis cough sound classification method based on Channel attention and multiscale Mel-spectrogram. • Adaptive scale audio feature extraction to compute different scales of window sizes and generate the multiscale Mel-spectrogram. • The proposed method can attain comparatively good performance. Croup and pertussis are major illnesses that result in human fatality, especially in pediatric patients. Timely and accurate diagnosis of these diseases is crucial to reducing mortality rates. Therefore, there is a need for a low-cost, rapid, and accurate diagnostic solution. This paper proposes a croup and pertussis cough sound classification algorithm based on channel attention and multiscale Mel-spectrogram (CPCSC). Firstly, an automatic croup and pertussis cough classification method is implemented. Secondly, an adaptive scale audio feature extraction method (ASFE) is proposed, which is used to compute different scales of window sizes and hop length to generate the multiscale Mel-spectrogram (MSMel-spectrogram). Thirdly, a CNN model with a channel attention mechanism is proposed to extract features of the MSMel-spectrogram. The channel attention mechanism captures channel information to enhance model performance. Finally, the comparison results with six methods on the cough dataset, CSC4, demonstrate that CPCSC outperforms other comparison algorithms with an average accuracy, sensitivity, specificity, precision, and F1-score of 90.5%, 90.5%, 93.92%, 91.37%, and 90.25%, respectively. [ABSTRACT FROM AUTHOR]

Physiological signals, such as electrocardiogram (ECG) and wrist pulse signals (WPS), play an important role in diagnosing and preventing cardiovascular and other physiological diseases. Therefore, accurate classification of physiological signals has become the key to assist physicians in diagnosis. However, this field still faces several prominent challenges, including limited availability of data, imbalanced datasets, convergence issues with loss functions, and the need for model architectures capable of accurately detecting waveform patterns. This study introduces the Physiological Signal Classification Network (PSC-Net), which combines the strengths of Convolutional Neural Networks (CNNs) and transformers for applications in medical artificial intelligence. Specifically, local temporal features are extracted using the GRWA-LSTM network (GLNet) proposed in this paper. Within the transformer, two GRU layers replace the fully-connected layer to enhance global feature extraction for physiological signal data. Residual connection integrates the outputs of GLNet and Transformer through global average pooling and weight settings. To address challenges related to small and imbalanced datasets, we propose an enhanced data augmentation algorithm based on SMOTE Tomek, along with an improved loss function. Additionally, automatic learning rates are optimized using the Dung Beetle Algorithm (DBA). Our proposed method achieves superior accuracies of 83.33%, 100.0%, 95.74%, and 98.85% on four physiological signal datasets (including one clinical dataset): Five Types of Pulses Database, Coronary Heart Disease (CHD) database, MIT-BIH Arrhythmia Database, and MIT-BIH ST Change Database. These results attest to the model's robust generalization capability and its promising application prospects in assisting diagnoses. [ABSTRACT FROM AUTHOR]

• This paper proposes the application of deep learning algorithms for diagnosing 14 major ophthalmological defects such as Hollenhorst Emboli, Arteriosclerotic Retinopathy etc. • In this study, multiple performance evaluation techniques such as Precision, Recall, F-1 Score, etc. are used to compare deep learning algorithms. • In this study, the performance compared with the existing literature achieved higher accuracy due to the unique model and its configuration, hyperparameter tuning and pre-processing techniques for the 14 classes of retinal defects. Retina is the heart of an eye which generates electrical impulses due to light sensitivity. The vessel formation in human eye is an essential key for diagnosing ophthalmological conditions. This paper aims to diagnose ophthalmological conditions through deep learning models and provide advancements in early detection of ophthalmological conditions for proper treatment to protect patient's vision, and for health care giver worldwide. STARE dataset is used for this study which consists over 385 retinal images of 14 ophthalmological defects such as BRAO, CRAO, etc. This dataset is further pre-processed over the techniques such as augmentation, normalization, etc for obtaining the best refined features for training deep learning algorithms. This paper broadly implements 5 deep learning algorithms i.e., EfficientNet, 3-Layers CNN, InceptionV2, ResNet-50, VGG-16. These models are trained number of times over tuned hyperparameters such as batch size etc and evaluated over 4 performance metrics over weighted averaged and macro averaged of precision, recall, F1-score, and accuracy to acquire the best performing model. EfficientNet performed the best with 98.43% accuracy, macro averaged f-1 score, recall, precision as 98.37%, 99.16%, 97.91% and weighted averaged f-1 score, recall, precision, as 98.50%, 98.43%, 98.82% over batch size 64. In this study, the performance compared with the existing literature achieved higher accuracy due to the unique model and its configuration, hyperparameter tuning and pre-processing techniques for the 14 classes of retinal defects. The future work includes classifying more ophthalmological conditions, adding more parameters from blood, etc. [ABSTRACT FROM AUTHOR]

[Display omitted] • Expert decision system will prove beneficial for Glaucoma screening at large scale. • CNN based framework can be used as assistant by clinicians and healthcare practioners in limited facilities. • SLIC and normalized graph cut algorithm in conjoint gives good results in OD segmentation. Accurately segmenting the Optic Disk is a crucial step in classifying Glaucoma using Fundus images. Machine learning and artificial intelligence techniques are widely used in Glaucoma detection, and the main indicators observed in Fundus images are the presence of Papillary Atrophy, Cup to Disc Ratio values, diminishing Neural Retinal Rim (NRR), the Inferior Superior Nasal Temporal (ISNT) rule, and Cup Diameter. In this research, we investigated various segmentation and classification techniques that can be applied to Optic disk segmentation and classification of normal and glaucomatous eyes. The proposed method will be beneficial to clinicians and healthcare workers in facilities with limited resources. In this paper, histogram processing is used to determine the type of image, and based on this information; we decide whether the image requires segmentation. Some datasets in the standard dataset contain complete retinal images while others include segmented optic disks. The segmented images are directly given as input for classification using the proposed Convolutional Neural Network (CNN). For complete retinal images, segmentation is performed using the Simple Linear Iterative clustering (SLIC) and normalized graph cut algorithm. The proposed framework's performance is compared with that of pretrained neural networks, including VGG19, InceptionV3, and ResNet50V2, using major metrics. We trained and tested these architectures with 3115 images from six standard datasets. Our proposed framework outperforms all with an accuracy of 96.33 %. [ABSTRACT FROM AUTHOR]

• A novel double U-shaped network that combines CNN and Swin Transformer, namely CSwinDoubleU-Net, which takes full advantage of complementary information of both convolution and self-attention is proposed for polyp segmentation. • The first U-shaped codecs network utilizes the multi-convolution layers to extract the local feature information and the coordinate attention module in each skip connection. • The second U-shaped codec employs the Swin transformer layer to extract the global feature information. • CSFFM between two U-shaped codecs can enhance multi-level feature representation by fusing the low-level and global-level features in an interactive embedding manner, which further improves the prediction capability of the model by retrieving the information lost during feature extraction. The early detection of polyps from colonoscopy can reduce the risk of colorectal cancer development and give timely treatment. Accurate polyp segmentation during colonoscopy examinations can help clinicians locate the polyps which is of great significance in the clinical prevention of colorectal cancer. However, due to large variations in terms of size, color, texture, and morphology of polyps, the similarity between the polyp lesions and their background, the variations of illumination, motion blur, low-contrast areas, intestinal contents during image acquisitions, precisely polyp segmentation is still an open issue. To overcome the challenges above, a new double U-shaped image segmentation network combining convolution structure and Shifted Windows (Swin) Transformer, namely CSwinDoubleU-Net, is proposed in this paper. The developed CSwinDoubleU-Net consists of two U-shaped encode-decode structures associated with a pure CNN-based structure and a pure Transformer-based one, and a feature fusion module that interacts with the two U-shaped parts. The first U-shaped codecs network utilizes the multi-convolution layers to extract the local feature information and the coordinate attention module in each skip connection to effectively reduce the loss of spatial information and obtain the accurate position information of the encoded features. Next, the second U-shaped codecs network uses the Swin Transformer layers with the sliding window to extract global feature information further. At last, a convolutional feature and self-attention feature fusion module (CSFFM) is designed to deeply fuse the local convolutional features extracted from the first U-shape structure and the global self-Attention features extracted from the second U-shape structure. The obtained multi-category and multi-dimensional fused feature information can help to recover the boundary features of polyps. Extensive experiments are conducted to validate the proposed CSwinDoubleU-Net on five publicly available datasets, including CVC-ClinicDB, Kvasir, CVC-ColonDB, CVC-T, and ETIS-Larib. The results show that the proposed model can outperform some state-of-the-art methods and achieve high segmentation performance for polyp images. [ABSTRACT FROM AUTHOR]

• The proposed TB-MFCC multifuse feature is comprised of data augmentation and feature extraction. • First, Pooled Multifuse Feature Augmentation uses the noise injection, stretching, shifting and pitching separately to increase the number of instances in the dataset and reduces overfitting. • Second, Triangular Bluestein Mel Frequency Cepstral Coefficients (TB-MFCC) are proposed and fused with Zero Crossing Rate (ZCR), Mel-frequency cepstral coefficients (MFCC) to extract the exact feature which increases the accuracy and reduces the Mean Squared Error (MSE). • Finally, the proposed MCNN with A-BiLSTM improves the nonlinear relationship among the features. • Thus the proposed augmentation, segmentation and classification is used to increase the accurate prediction of vehicle with reduced loss, less chance of overfitting and underfitting and avoids vanishing gradient problems. Vehicles equipped for emergencies like ambulances, fire engines, and police cruisers play a vital role in society by responding quickly to emergencies and helping to prevent loss of life and maintain order. Vehicle sound identification and classification are very important in the cities to identify emergency vehicles easily and to clear the traffic effectively. Convolutional Neural Network plays an important role in the accurate prediction of vehicles during an emergency. The main motive of this paper is to develop a suitable model and algorithms for data augmentation, feature extraction, and classification. The proposed TB-MFCC multifuse feature is comprised of data augmentation and feature extraction. First, in the proposed signal augmentation, each audio signal uses noise injection, stretching, shifting, and pitching separately and this process increases the number of instances in the dataset. The proposed augmentation reduces the overfitting problem in the network. Second, Triangular Bluestein Mel Frequency Cepstral Coefficients (TB-MFCC) are proposed and fused with Zero Crossing Rate (ZCR), Mel-frequency cepstral coefficients (MFCC), Root Mean Square (RMS), Chroma, and Tempogram to extract the exact feature which increases the accuracy and reduces the Mean Squared Error (MSE) of the model during classification. Finally, the proposed Multi-stacked Convolutional Neural Network (MCNN) with Attention-based Bidirectional Long Short Term Memory (A-BiLSTM) improves the nonlinear relationship among the features. The proposed Pooled Multifuse Feature Augmentation (PMFA) with MCNN & A-BiLSTM increases the accuracy (98.66 %), reduces the False Positive Rate (FPR) by 1.01 %, and loss (0 %). Thus the model predicts the sound without overfitting, underfitting, and vanishing gradient problems. [ABSTRACT FROM AUTHOR]

Brain tumors are one of the most threatening causes of death worldwide. Neuroradiologists widely use Magnetic Resonance Imaging as a golden standard non-invasive imaging method for diagnosing Glioma grades. Brain tumor diagnosis based on radiology images is tedious and highly subjective to intra and inter-observer variability; it adversely affects therapeutic planning. Better medical treatment can be provided relating to similar past cases if relevant images are retrieved from an extensive medical image database. Content-Based Image Retrieval systems are potent image tools for dealing with such massive datasets. In a CBIR system, accurate classification and retrieval of similar pathological images can be effectively automated by utilising Convolutional Neural Network-based feature extraction methods. This paper presents a Content-Based Medical Image Retrieval pipeline on a medical domain using the CNN model for feature extraction and the clustering method used to index the feature map database. The proposed system applies a Multi-level Gain-based feature selection to reduce the dimensionality of the feature vectors obtained from the pre-trained CNN models. The experiment follows five-fold cross-validation on BraTS 2018 and 2020 datasets. The proposed system achieves state-of-the-art MRI brain image retrieval performance with a mean Average Precision and Precision@10 of 98.15% and 97.62%, respectively. The results prove the proposed method's effectiveness and validate its clinical application. • Inventing a proper CBMIR pipeline for medical domain with state-of-the-art efficiency. • Figured out relevant components of the pipeline using theoretical and experimental trials. • Proposed a novel feature space reduction strategy using Multi-level Gain-based feature selection. • Feature vector indexing for fast and efficient MR image retrieval from large database. [ABSTRACT FROM AUTHOR]

• A unique detection and classification method (DCCNet) for fast diagnosis of COVID-19 patients was presented. • CNN and HOG based feature extractors were used for feature extraction. • YOLOv3 was used to cross check if the input image is a chest X-ray image of a human lung or not. • Combination of CNN and HOG-based features with the SVM classifier achieved 99.97% training accuracy and 99.67% testing accuracy. COVID-19 is now regarded as the most lethal disease caused by the novel coronavirus disease of humans. The COVID-19 pandemic has spread to every country on the planet and has wreaked havoc on these countries by increasing the number of human deaths, and in addition, caused intense hunger, and lowered economic productivity. Due to a lack of sufficient radiologist, a restricted amount of COVID-19 test kits is available in hospitals, and this is also accompanied by a shortage of equipment due to the daily increase in cases, as a result of increase in the number of persons infected with COVID-19. Even for experienced radiologists, examining chest X-rays is a difficult task. Many people have died as a result of inaccurate COVID-19 diagnosis and treatment, as well as ineffective detection measures. This paper, therefore presents a unique detection and classification approach (DCCNet) for quick diagnosis of COVID-19 using chest X-ray images of patients. To achieve quick diagnosis, a convolutional neural network (CNN) and histogram of oriented gradients (HOG) method is proposed in this paper to help medical experts diagnose COVID-19 disease. The diagnostic performance of the hybrid CNN model and HOG-based method was then evaluated using chest X-ray images collected from University of Gondar and online databases. The experiment was performed using Keras (with TensorFlow as a backend) and Python. After the DCCNet model was evaluated, a 99.9% training accuracy and 98.3% test accuracy was achieved, while a 100% training accuracy and 98.5% test accuracy was achieved using HOG. After the evaluation, the hybrid model achieved 99.97% and 99.67% training and testing accuracy for detection and classification of COVID-19 which was better by 1.37% compared to when features were extracted using CNN and 1.17% when HOG was used. The DCCNet achieved a result that outperformed state-of-the-art models by 6.7%. [ABSTRACT FROM AUTHOR]

• We introduce a novel seq2seq network architecture based on a lightweight Transformer that combines two types of feature embedding networks, CNN and DAE. Each heartbeat is mapped to a vector, and multiple consecutive heartbeats are aligned into a vector sequence. Heartbeats can enhance their feature representation by attending to adjacent heartbeats. • We provide a denoising autoencoder with multiple regularizations for local feature embedding and compare various transfer modes, including the introduction of an external large dataset with different attributes to the training process. These techniques help mitigate the interdomain difference between the train set and the test set. • We present a well-designed feature network based on CNN, and its combination with the Transformer achieves promising results when using only the train set for network parameter optimization under the standard test protocol. Researchers have proposed numerous novel features and models under the intra-patient paradigm. However, their performance suffers when considering the inter-patient paradigm. While some state-of-the-art results have been reported in recent years under the inter-patient paradigm, many of them deviate from the standard test protocol. The performance of minority classes remains unsatisfactory for practical applications under strict test protocols. This paper presents a novel framework based on a lightweight Transformer combined with CNN and a denoising autoencoder, which enhances the performance of minority classes under the standard test protocol. The proposed model includes a new seq2seq network that extracts local features from a single heartbeat using CNN or a denoising encoder, and attends to global features from neighboring heartbeats based on a lightweight Transformer encoder. In particular, we pretrained the autoencoder on the MIT-BIH dataset and an additional dataset, considering several transfer modes for feature representation. We organized multiple continuous heartbeats into a vector sequence, where each heartbeat incorporates information from its neighbors to improve feature representation. The model evaluation was conducted using the MIT-BIH inter-patient dataset, following the AAMI standard. The Transformer with CNN embedding achieved a total accuracy of 97.66% on the test set, while the Transformer with pretrained denoising autoencoder achieved a total accuracy of 97.93%. These results demonstrate the promising performance of our models for imbalanced inter-patient ECG classification under the standard test protocol. [ABSTRACT FROM AUTHOR]

• We introduce a novel seq2seq network architecture based on a lightweight Transformer that combines two types of feature embedding networks, CNN and DAE. Each heartbeat is mapped to a vector, and multiple consecutive heartbeats are aligned into a vector sequence. Heartbeats can enhance their feature representation by attending to adjacent heartbeats. • We provide a denoising autoencoder with multiple regularizations for local feature embedding and compare various transfer modes, including the introduction of an external large dataset with different attributes to the training process. These techniques help mitigate the interdomain difference between the train set and the test set. • We present a well-designed feature network based on CNN, and its combination with the Transformer achieves promising results when using only the train set for network parameter optimization under the standard test protocol. Researchers have proposed numerous novel features and models under the intra-patient paradigm. However, their performance suffers when considering the inter-patient paradigm. While some state-of-the-art results have been reported in recent years under the inter-patient paradigm, many of them deviate from the standard test protocol. The performance of minority classes remains unsatisfactory for practical applications under strict test protocols. This paper presents a novel framework based on a lightweight Transformer combined with CNN and a denoising autoencoder, which enhances the performance of minority classes under the standard test protocol. The proposed model includes a new seq2seq network that extracts local features from a single heartbeat using CNN or a denoising encoder, and attends to global features from neighboring heartbeats based on a lightweight Transformer encoder. In particular, we pretrained the autoencoder on the MIT-BIH dataset and an additional dataset, considering several transfer modes for feature representation. We organized multiple continuous heartbeats into a vector sequence, where each heartbeat incorporates information from its neighbors to improve feature representation. The model evaluation was conducted using the MIT-BIH inter-patient dataset, following the AAMI standard. The Transformer with CNN embedding achieved a total accuracy of 97.66% on the test set, while the Transformer with pretrained denoising autoencoder achieved a total accuracy of 97.93%. These results demonstrate the promising performance of our models for imbalanced inter-patient ECG classification under the standard test protocol. [ABSTRACT FROM AUTHOR]

[Display omitted] • A convolution operation in CNNs enable networks to derive significant features using spatial and channel-wise information. A wide range of prior research has canvassed the spatial element to strengthen the representational power of CNN. • In this paper, instead of spatial element we centered our research on channel-wise element. The UNet (38) is state-of-the-art medical image segmentation network which has encoder-decoder structure with number of covolutional layers. For segmentation of tiny structures like nodules, UNet lacks in representation of significant features. The UNet architecture has long skip connections between encoder and decoder at various levels in order to retain the spatial information. • To strengthen the representation power of UNet, we induced the short skip connections called residual connections in the encoder, thereby forming a residual variant of Unet. However, these modifications were not sufficient for effective nodule segmentation. Therefore, we introduce a further modified and improved version of UNet which is termed as SquExUNet. It is a novel nodule segmentation model that utilizes the effectiveness of squeeze and excitation blocks to extract fine-grained information to segment the nodules. • The segmentation framework proposed in this paper utilizes SquExUNet and segments the nodule effectively, but the segmentation framework does emphasize on detect-ability of nodules. Consequently, there arises a need of classification network that can classify the nodule candidates yielded in segmentation framework. • The nodules and non nodule structures are difficult to distinguish in 2D image due to their similar resemblance. Taking that into consideration, we presented an unique approach for detection of nodules using 2D-3D cascaded CNNs. In this detection framework, primarily segmentation framework is used for collection of nodule candidates; and thereafter, a novel 3D classification model termed as 3D-NodNet classifies nodules and non-nodule volumetric cubes. • A novel substantial amount of Indian Lung CT Image Database (ILCID) clinical dataset is included in the study along with publicly available datasets. The early detection of lung cancer is attained with the detection of initial stage nodules (3 − 30 mm) which can exorbitantly increase the 5 -year survival rate of lung cancer patients. Nodules are very small size circumscribed structures in the lungs and are difficult to detect due to their size. The identification of nodule is also more challenging due to similarly resembling structures like non-nodules that contains features which could make it identifiable as a nodule. Therefore, to deal with these challenging issues, we proposed a novel approach for segmentation, classification and detection of lung nodules from CT scan images. In our proposed method we involve a maximum intensity projection technique as a part of image preprocessing method. We demonstrated our experimentation and proposed SquExUNet segmentation model and 3D-NodNet classification model on publicly available Lung Image Database Consortium – Image Database Resource Initiative (LIDC), LNDb Challenge Dataset and purely independent Indian Lung CT Image Database (ILCID) clinical dataset. We proposed a 2D-3D cascaded CNN strategy for detection of nodule that yields the accurately segmented and classified nodule. Results obtained with proposed method indicates that we have successfully detected and segmented the lung nodules effectively, compared to existing lung nodule detection and segmentation algorithms. We achieved a Dice-Coefficient metrics of 0.80 for segmentation of nodule and 90.01 % Sensitivity for nodule detection. [ABSTRACT FROM AUTHOR]

• A cataract detection model proposed using anterior segment eye images rather than fundus image. • This approach can be used to make a portable diagnostic system using mobile and desktops. • The evaluation was made by targeting iris and pupil as our Region Of Interest. • Adaptive thresholding is then applied to extract the distinguishing features. • A fine-tuned Inception-v3 model is constructed to get the final output classification result. Early detection of cataracts plays a vital role in ensuring the prevention of vision loss. This paper aims to propose an algorithm that will act as an assistive measure in the process of automating cataract disease detection. The majority of the existing works are focused on the utilization of either fundus images, slit lamp images, or visible wavelength images captured using a DSLR camera. The novelty of this proposed algorithm is the capability to deliver equally accurate and precise performance using both normally captured visible wavelength images as well as medically captured anterior segment images which can, in turn, prove to be cost-effective as well. The image pre-processing techniques particularly adaptive thresholding hereby employed provides fast and accurate results on the input dataset fed to the CNN model which in turn is a fine-tuned version of Inception-v3. Here the training of the model has been done using visible wavelength images whereas the validation testing has been done using the anterior segment eye images medically obtained from a hospital. The proposed image pre-processing technique along with the model architecture ensures the achievement of a high classification accuracy of about 95%. Since the model deals with the examination of the anterior segment of the image, cases concerning nuclear cataract, cortical cataract, or a hybrid case involving the detection of both the aforementioned cataract types lie under the range of detection of our system. [ABSTRACT FROM AUTHOR]

• Proposed tunable-Q wavelet method to extract approximate and details of signal and remove redundant information. • Improved the robustness of EEG based epilepsy detection using the deep learning method with CNN model. • Proposed method can achieve 97.57% accuracy, 98.90% sensitivity, 2.10% false positive rate and 10.46-second delay in the real-time seizure detection in testing data. • Combined signal processing method and image classification in this experiment. • Implemented an automatic real-time seizure detection implementation. Epilepsy is a chronic disease caused by sudden abnormal discharge of brain neurons, leading to transient brain dysfunctions. This paper proposed an EEG based real-time approach to detect epilepsy seizures using tunable-Q wavelet transform and convolutional neural network (CNN). Statistical moments and spectral band power were used to reveal the time domain and frequency domain features in EEG, and then were converted into imaged-like data fed into CNN. The proposed approach was evaluated using the database CHB-MIT. The proposed algorithm achieved 97.57% in accuracy, 98.90% in sensitivity, 2.13% in false positive rate and 10.46-second delay. In addition, the proposed method is suitable in real-time implementation. The outcomes indicate that the proposed method can applied to real-time seizure detection in clinical applications. [ABSTRACT FROM AUTHOR]

Heart disease is one of the top ten threats to global health in 2019 according to the WHO. Continuous monitoring of ECG on wearable devices can detect abnormality in the user's heartbeat early, thereby significantly increasing the chance of early intervention which is known to be the key to saving lives. In this paper, we present a set of inter-patient ECG classification methods that use convolutional (CNNs) and spiking neural networks (SNNs). We focused on inter-patient heartbeat classification, in which the model is trained over several patients and then used to infer that for patients not used in training. Raw heartbeat data is used in this paper because most wearable devices cannot deal with complex data preprocessing. A two-steps convolutional neural network testing method is proposed for saving power. For even greater energy-saving, a spiking neural network is also proposed. The latter is obtained from converting the trained CNN model with a less than one percent accuracy drop. The average power of a two-classes SNN is 0.077 W, or 0. 0074 × that of previously proposed neural network-based solutions. [ABSTRACT FROM AUTHOR]

• An end-to-end method based on convolutional neural network and transformer (ECT-net) is proposed to detect ECG characteristic waveform under the inter-patient pattern. • Transformer is applied to detect ECG characteristic waveform for the first time. • The proposed method shows excellent performance in different pathological ECG datasets. The electrocardiogram (ECG) characteristic waveforms include P wave, QRS wave, and T wave. The detection of ECG characteristic waveforms is particularly important in automatic heart disease diagnosis. Although researchers have proposed many detection methods, the detection under the inter-patient pattern is still a challenge. This paper proposed an end-to-end ECG waveform detection method (ECT-net) based on convolutional neural network (CNN) and transformer. We constructed the feature extractor by a group of convolutional layers to extract the local information of ECG signals and generate the input embedding of the transformer encoder. Meanwhile, we adopted the transformer encoder to extract the long-term time-dependent representation between heartbeats, which can make up for the limitation of the CNN in obtaining global information. This structure ensures that both spatial and temporal features from ECG signals are achieved. For precise detection, we employed a corresponding decoder and skip structure to integrate low-level features and high-level features. The model was trained and verified on the China Physiological Signal Challenge 2018 database (CPSC-DB). Our method performed best among comparison methods and achieved F1 scores of 94.27% on the P wave, 97.32% on the QRS wave, and 93.92% on the T wave. The ablation result shows that the transformer encoder is beneficial for detecting each characteristic waveform. To assess the generalization ability of the method, it was also evaluated on inter-patient datasets with different types of cardiac diseases, and experimental results demonstrated that the proposed method is effective for all of them. The proposed method applies the transformer encoder to the detection of ECG characteristic waveforms for the first time and achieves competitive performance. [ABSTRACT FROM AUTHOR]

Sleep stage scoring is essential for the diagnosis and treatment of sleep disorders. However, manual sleep scoring is a tedious, time-consuming, and subjective task. Therefore, this paper proposes a novel framework based on local pattern transformation (LPT) methods and convolutional neural networks for automatic sleep stage scoring. Unlike in previous works in other fields, these methods were not employed for manual feature extraction, which requires expert knowledge and the pipeline behind it might bias results. The transformed signals were directly fed into a CNN model (called EpochNet) that can accept multiple successive epochs. The model learns features from multiple input epochs and considers inter-epoch context during classification. To evaluate and validate the effectiveness of the proposed approach, we conducted several experiments on the Sleep-EDF dataset. Four LPT methods, including One-dimensional Local Binary Pattern (1D-LBP), Local Neighbor Descriptive Pattern (LNDP), Local Gradient Pattern (LGP), and Local Neighbor Gradient Pattern (LNGP), and different polysomnography (PSG) signals were analyzed as sequence length (number of input epochs) increased from one to five. 1D-LBP and LNDP achieved similar performances, outperforming other LPT methods that are less sensitive to local variations. The best performance was achieved when an input sequence containing five epochs of PSG signals transformed by 1D-LBP was employed. The best accuracy, F1 score, and Kohen's kappa coefficient were 0.848, 0.782, and 0.790, respectively. The results showed that our approach can achieve comparable performance to other state-of-the-art methods while occupying fewer computing resources because of the compact size of EpochNet. [ABSTRACT FROM AUTHOR]

Melanoma is the fastest growing and most lethal cancer among all forms of skin cancer. Deep learning methods, mainly convolutional neural networks (CNNs) have recently brought considerable attention in detecting skin cancers from dermoscopy images. However, learning valuable features by these methods has been challenging due to the inadequate training data, inter-class similarity, and intra-class variation in the skin lesions. In addition, most of these methods demand a considerable amount of parameters to tune. To address these issues, we present an automated framework that extracts visual features from dermoscopy images using a pre-trained deep CNN model and then employs a set of classifiers to detect melanoma. Recently, few pre-trained CNN architectures have been employed to extract deep features from skin lesions. However, a comprehensive analysis of such features derived from a variety of CNN architectures has not yet been performed for melanoma classification. Therefore, in this paper, we investigate the effectiveness of deep features extracted from eight contemporary CNN models. Also, we explore the impact of boundary localization and normalization techniques on melanoma detection. The suggested approach is evaluated using four benchmark datasets: PH 2 , ISIC 2016, ISIC 2017 and HAM10000. Experimental outcomes indicate that the DenseNet-121 with multi-layer perceptron (MLP) achieves a higher performance in terms of accuracy of 98.33%, 80.47%, 81.16% and 81% on PH 2 , ISIC 2016, ISIC 2017 and HAM10000 datasets compared to other CNN models and state-of-the-art methods. • An automated method for melanoma diagnosis is proposed using deep CNN features. • A comprehensive analysis among eight pre-trained CNN models is performed. • Ablation studies are performed to show the impact of several influencing factors. • Results show the superiority of the proposed framework over the existing methods. • The model could assist dermatologists for accurate diagnosis of melanoma. [ABSTRACT FROM AUTHOR]

Melanoma is the fastest growing and most lethal cancer among all forms of skin cancer. Deep learning methods, mainly convolutional neural networks (CNNs) have recently brought considerable attention in detecting skin cancers from dermoscopy images. However, learning valuable features by these methods has been challenging due to the inadequate training data, inter-class similarity, and intra-class variation in the skin lesions. In addition, most of these methods demand a considerable amount of parameters to tune. To address these issues, we present an automated framework that extracts visual features from dermoscopy images using a pre-trained deep CNN model and then employs a set of classifiers to detect melanoma. Recently, few pre-trained CNN architectures have been employed to extract deep features from skin lesions. However, a comprehensive analysis of such features derived from a variety of CNN architectures has not yet been performed for melanoma classification. Therefore, in this paper, we investigate the effectiveness of deep features extracted from eight contemporary CNN models. Also, we explore the impact of boundary localization and normalization techniques on melanoma detection. The suggested approach is evaluated using four benchmark datasets: PH 2 , ISIC 2016, ISIC 2017 and HAM10000. Experimental outcomes indicate that the DenseNet-121 with multi-layer perceptron (MLP) achieves a higher performance in terms of accuracy of 98.33%, 80.47%, 81.16% and 81% on PH 2 , ISIC 2016, ISIC 2017 and HAM10000 datasets compared to other CNN models and state-of-the-art methods. • An automated method for melanoma diagnosis is proposed using deep CNN features. • A comprehensive analysis among eight pre-trained CNN models is performed. • Ablation studies are performed to show the impact of several influencing factors. • Results show the superiority of the proposed framework over the existing methods. • The model could assist dermatologists for accurate diagnosis of melanoma. [ABSTRACT FROM AUTHOR]

• Based on swarm intelligence, this paper presents a method for optimizing deep learning architectural models. • The proposed work combines CNNs with a statistical operator-based improvement to the glow-worm swarm algorithm. • Pretrained VGGNet is employed for extracting features. • VGGNet's final layer can be deleted, allowing the image to pass through the network to acquire the feature vector. • Feature optimization is performed using Statistically Enhanced Glow-worm Swarm Algorithm (SEGSO). Efficient classification of glaucoma from fundus images remains crucial and a challenging task as the retinal anatomical structure is so complex in nature with varying contrast and boundaries. As a result, there is a chance that expert systems will misclassify the data. As a way to reduce the misclassification rate, a methodology wherein a deep learning approach integrated with an evolutionary algorithm is proposed. First, the relevant features are extracted using a pre-trained ImageNet model, the VGGNet. The features extorted are subsequently filtered using statistically enhanced Glow-worm Swarm Optimization (SEGSO). The algorithm identifies the most important and relevant features, while excluding those that are noisy or highly correlated. The efficiency of this fully automated system is evaluated on public and private retinal fundus image databases (totally 18,879 images) and the experiments demonstrated a high accuracy of 99.6% with less computational complexity, better sensitivity and specificity. SEGSO algorithm selected comparatively lesser features than the similar algorithms and when, employed with VGGNet outperform several other convolutional network models. The proposed method is robust against salt-pepper noise and achieved an accuracy of 97.2% when applied on degraded images. Feature extraction using Convolutional Neural Network and SEGSO feature optimization could prove to be a good combination to be used for other biomedical image classification processes. [ABSTRACT FROM AUTHOR]

[Display omitted] • First, we collected EEG signals from 40 depressed patients and 40 healthy controls and eliminated the noise through wavelet transformation. • Second, the EEG signals are divided into 5 frequencies and fed into mutiple models to evaluate the performance. • Third, we propose a model named LSDD-EEGNet for EEG-based depression detection, which connects CNN and LSTM layer as the extractor for EEG signals and introduces the domain discriminator to deal with the feature shift problem between training dataset and test dataset. • Finally, we evaluate the performance of LSDD-EEGNet and compare the model with other representative models. Depression is a mood disorder that causes negative effects on people's life and has become a leading health burden worldwide. But the effective and low-cost detection for depression is still a great challenge. Electroencephalogram (EEG) measures the brain activities and can be used for depression-related research. In this paper, we propose an effective end-to-end framework named LSDD-EEGNet for EEG-based depression detection. Specially, LSDD-EEGNet has two distinguishing characteristics for depression recognition: (1) Considering the superiority of convolution neural network (CNN) on feature extraction and the efficiency of long-short term memory (LSTM) for time-series signals, we combine both as the extractor for LSDD-EEGNet. (2) We apply the domain discriminator to modify the data representation space and eliminate the discrepancy between training and test dataset. In addition, we collected EEG signals from 40 depressed patients (DPs) and 40 healthy controls (HCs) to evaluate the performance of the proposed deep framework for depression detection. Compared to other typical machine learning (ML) methods and deep learning (DL) models, LSDD-EEGNet achieves superior performance on subject-independent evaluation that shows the LSDD-EEGNet can be a promising detection method for depression. [ABSTRACT FROM AUTHOR]

Biometric Systems are automatic methods of verifying the identity of a person based on some characteristics such as fingerprint, face, speech etc. Speech biometrics involves verifying the identity of a person based on voice. The process is called speaker verification, and such biometric systems are called Automatic Speaker Verification systems. However, these systems are prone to malicious attacks such as replay attacks. Thus, Automatic Speaker Verification systems need to be robust against these attacks. Existing approaches have worked majorly with single-replay attacks. The proposed work in this paper explores three back-end models: Convolutional Neural Network, Long Short-Term Memory and hybrid of these two models, with different input feature formats. It analyses their performance in a diverse multi-replay attack detection scenario, using the Voice Spoofing Detection Corpus. In the frontend of the proposed hybrid system, mel -spectrograms is used as the feature extraction technique for Convolutional Neural Network based model, and Constant-Q Cepstral Coefficient approach is used for extracting features for Long Short-Term Memory based model. A comparison of this hybrid model's performance is done with past approaches in single-replay attack detection as well. The average Equal Error Rate achieved on the test set was 0.036 for single-replayed attack and 0.0296 for two times replayed. The hybrid model consistently outperforms other models with lower Equal Error Rate values, thus showing promising results, paving the way for future research along this line. Further, an analysis on unseen noisy audio files suggests the proposed model's utility in noise-robust systems as well. [ABSTRACT FROM AUTHOR]

[Display omitted] • Energy kernel phase portrait is extracted for sEMG image processing. • Amplitude image method is used for IMU data processing instead of calculating the angles. • The dual-stream CNN is built in the feature fusion of sEMG and IMU. Since the surface electromyography signal (sEMG) is a weak and unstable signal, human action recognition based on sEMG time domain or frequency domain feature extraction has poor stability and low recognition rate. Based on modeling analysis of sEMG, the dual-stream convolutional neural network (CNN) is built to perform feature fusion of sEMG energy kernel phase portrait and inertial measurement unit(IMU) data for gesture recognition in this paper. Firstly, methods of matrix counting method and time window amplitude are utilized to represent the sEMG and IMU data as images. Secondly, the moving average method is presented to filter the images. Thirdly, the dual-stream CNN is used to perform feature extraction, fusion and classification of sEMG and IMU images respectively. Finally, the experiments yield an average recognition accuracy of 95.78% for 6 gestures performed by 5 subjects, which demonstrates that the proposed method has better performance for human gestures recognition. [ABSTRACT FROM AUTHOR]

• Convolutional neural network and Long Short-term Memory Network are connected in parallel to form a fusion network. • The spatial feature, temporal feature and middle layer feature are extracted and fused integrally. • The average accuracy and Kappa value of the proposed algorithm are 87.68% and 0.8245, respectively. Motor imagery brain-computer interface (MI-BCI) provides a novel way for human-computer interaction. Traditional neural networks often use serial structure to extract spatial features when dealing with motor imagery EEG signal classification, ignoring temporal information and a large amount of available information in the middle layer, resulting in poor classification performance of MI-BCI. A neural network feature fusion algorithm is proposed by combining the convolutional neural network (CNN) and the long-short-term memory network (LSTM). Specifically, the CNN and LSTM are connected in parallel. The CNN extracts spatial features, the LSTM extracts temporal features and the flatten layer added after the convolutional layer extracts the middle layer features. Then all the features are merged in the fully connected layer to improve the accuracy of classification. The average accuracy and Kappa value of all subjects were 87.68% and 0.8245, respectively. The result shows that the feature fusion neural network proposed in this paper can effectively improve the accuracy of motor imagery EEG, and provides new ideas for the study of feature extraction and classification of motor imagery brain-computer interfaces. [ABSTRACT FROM AUTHOR]

• A modified PSP segmentation model is proposed for delineating cell components. • The efficacy of nuclear as well as cell features on ml classifiers is investigated. • With the integration of Stack classifier, the robustness of the model is ensured. • Binary and Bethesda classification are evaluated on the publicly available dataset. Abnormal cell recognition from cervical pap cytology is crucial for early cervical cancer screening and treatment. However, the current manual analysis requires the undivided attention of the pathologists and is prone to diagnostic errors. To tackle this issue, several techniques have been proposed in the last few decades. The success of an automation-based cervical screening and diagnosis system relies on accurate identification of cell types. This paper proposes an efficient automated hybrid framework for enhancing the cell classification accuracy of cervical cytology images. In this work, a Pyramid Scene Parsing Model is used for the segmentation of cell components. The integration of the feature pyramid with local and global context prior makes the model suitable for the segmentation of small cell components. Nuclear as well as cell features of segmented regions are extracted and categorized using an ensemble-based stack classifier. The performance of five different machine learning classifiers is also investigated and it was found that the CNN-based segmentation followed by ensemble-based stack classifier achieved the best performance. The model was evaluated on a publicly available data set and achieved an average accuracy and AUC of 99.7% and 0.996 for 2-class classification and 75.55% and 0.909 for 4-class classification respectively. The experiments demonstrate that the proposed framework achieves promising performance on pap stained cytology images. [ABSTRACT FROM AUTHOR]

• The GA-RPN module is employed to generate candidate proposals that are more suitable for nuclei detection. • An IoU branch is proposed to regress the IoU between the detection boxes and their corresponding ground truth. • A fusioned box score is proposed as the ranking keyword in SoftNMS to select proper prediction bounding boxes. • The proposed method achieves improved nuclei detection and segmentation performances. Automated detection and segmentation of nuclei in microscopy images are of significant importance to biomedical research and clinical practice, including nuclear morphology analysis, cancer diagnosis and grading. However, these tasks are still challenging due to large numbers of adhered and clustered nuclei. Modern CNN-based nuclei detection and segmentation methods rely on bounding box regression and non-maximum suppression to locate the nuclei, which would lead to inferior localized bounding boxes of the adhered and clustered nuclei. In this paper, we propose a region-based convolutional network to tackle this challenge. In particular, a GA-RPN module integrating the guided anchoring(GA) into the region proposal network(RPN) is employed to generate candidate proposals that are more suitable for nuclei detection. A new branch is proposed to regress the intersection over union(IoU) between the detection boxes and their corresponding ground truth for locating the bounding box. To reduce the undetection of adhered and clustered nuclei, we pass a fusioned box score(FBS) into soft non-maximum suppression(SoftNMS) to preserve the true positive candidate boxes. The experiments are performed on two public challenging datasets, which are designed to challenge an algorithm's ability to generalize across different varieties. The results empirically demonstrate that our method has better detection and segmentation capability than existing state-of-the-art methods. In conclusion, our method can improve the performances and provide a potential for sophisticated cell-level analysis within digital tissue images. [ABSTRACT FROM AUTHOR]

• Feature Selection process perform better selection from UCI heart disease dataset. • ResNet-50 classifier approach is used for secured transmission of heart disease features. • Enhanced ElGamal method for encryption and decryption of data with key generation. • ResNet-50 with near 50 layers classifier for better classification process. • Comparative analysis in terms of performance metrics are performed. Heart disease is increasing, and their detection is a significant concern. With the several technologies developed, the approaches for detection mechanisms can be improved further with better and improved algorithms. Data related with patient's health are of huge amount and stored in the larger space of cloud storage. Accessing cloud storage is an easy task, where the data stored is available to many users of cloud and there comes the need of security. Generally security can be improved using encryption and decryption algorithms. In this paper, a framework using the ResNet-50 classifier approach for secured transmission of heart disease features is performed. This study focused on an enhanced ElGamal encryption-decryption method for the encryption of data with a generated private key and a public key for decryption to better access the data. The data encrypted are then decrypted when the user request data. With Convolutional Neural Network classifier of ResNet-50 with its near 50 layers, the refinement or classification process is performed. The heart disease dataset from the UCI heart disease repository is considered for the evaluation of the proposed work. Further, with feature selection method, better selection of input can be filtered from the selected dataset. The results are obtained with respect to various performance measures, then compared and analyzed with some of the existing methodologies. The results proved to be better than other existing frameworks. [ABSTRACT FROM AUTHOR]

• An automated breast cancer detection method is proposed. • The proposed deep CNN model requires less learnable parameters. • The CNN helps to learn discriminant features automatically from the mammogram and ultrasound images. • The model achieves greater classification accuracy over both mammogram and ultrasound images. This paper proposes a deep convolutional neural network (CNN) model for automated breast cancer classification from a different class of images, namely, mammograms and ultrasound. The model contains only five learnable layers: four convolutional layers and a fully connected layer. The model facilitates extracting prominent features automatically from the images with a smaller number of tunable parameters. Exhaustive simulation results on mammograms dataset, namely, MIAS, DDSM, and INbreast, as well as ultrasound datasets, namely, BUS-1 and BUS-2, depict that the suggested model outperforms the recent state-of-the-art schemes. Data augmentation technique has been employed to reduce overfitting and provide good generalization. The proposed CNN model achieves an accuracy of 96.55%, 90.68%, and 91.28% on MIAS, DDSM, and INbreast datasets, respectively. Similarly, the accuracies obtained are 100% and 89.73% on BUS-1 and BUS-2 datasets, respectively. [ABSTRACT FROM AUTHOR]

[Display omitted] • Deep learning methods were applied to automatically detect OSA from ECG signal. • Three deep learning techniques including CNN, LSTM, and CNN-LSTM were designed. • The proposed method obtained an accuracy of 97.21% in per segment classification. • Improving automatic ECG-based OSA detection accuracy and specificity. • The method outperforms existing state-of-the-art methods. Sleep apnea causes frequent cessation of breathing during sleep. Feature extraction approaches play a key role in the performance of apnea detection algorithms that use single-lead electrocardiogram signals. Handcrafted features have high computational complexity due to their large dimensions and are usually not robust. To cope with the mentioned problems, in the current paper, an automatic feature extraction method is developed by combining the Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) recurrent network. Also, the fully connected layers are utilized to distinguish apnea events from the normal segments. Then, the apnea-hypopnea index (AHI) is applied to discriminate apnea subjects from healthy ones. Finally, in order to assess the usefulness of the proposed method, some experiments are conducted on the publicly accessible Apnea-ECG and UCDDB datasets. The results based on the sensitivity (94.41%), specificity (98.94%), and accuracy (97.21%), indicate that our proposed method provides significant improvements compared to the other sleep apnea detection methods. Our model also achieves an accuracy of 93.70%, sensitivity of 90.69%, and specificity of 95.82% for UCDDB dataset. It can be inferred that using the deep-learning based algorithm for detecting apnea patients would help physicians in making a decision more accurately. [ABSTRACT FROM AUTHOR]

• We ourselves collected the data. So, the dataset is new. • Proposed method was tested on six public datasets and the results are better than most of state of the art papers. • We showed that using the Sobel filter can improve the performance of CNN. • We did not use pre-trained networks unlike most of the other researches. We ourselves designed the network. • Our method performance was better than the compared references. The coronavirus (COVID-19) is currently the most common contagious disease which is prevalent all over the world. The main challenge of this disease is the primary diagnosis to prevent secondary infections and its spread from one person to another. Therefore, it is essential to use an automatic diagnosis system along with clinical procedures for the rapid diagnosis of COVID-19 to prevent its spread. Artificial intelligence techniques using computed tomography (CT) images of the lungs and chest radiography have the potential to obtain high diagnostic performance for Covid-19 diagnosis. In this study, a fusion of convolutional neural network (CNN), support vector machine (SVM), and Sobel filter is proposed to detect COVID-19 using X-ray images. A new X-ray image dataset was collected and subjected to high pass filter using a Sobel filter to obtain the edges of the images. Then these images are fed to CNN deep learning model followed by SVM classifier with ten-fold cross validation strategy. This method is designed so that it can learn with not many data. Our results show that the proposed CNN-SVM with Sobel filter (CNN-SVM + Sobel) achieved the highest classification accuracy, sensitivity and specificity of 99.02%, 100% and 95.23%, respectively in automated detection of COVID-19. It showed that using Sobel filter can improve the performance of CNN. Unlike most of the other researches, this method does not use a pre-trained network. We have also validated our developed model using six public databases and obtained the highest performance. Hence, our developed model is ready for clinical application. [ABSTRACT FROM AUTHOR]

• The hybrid CNN-LSTM approach provides the best combination of performance (sensitivity, specificity) in comparison with all previous relevant studies. • The proposed model performs well for highly imbalanced datasets. • Focal loss function delivers better results than the classic cross-entropy function for ECG classification. • The proposed methodology could be used for real-time arrhythmia detection as the prediction phase lasts only a few seconds. Atrial fibrillation is a heart arrhythmia strongly associated with other heart-related complications that can increase the risk of strokes and heart failure. Manual electrocardiogram (ECG) interpretation for its diagnosis is tedious, time-consuming, requires high expertise, and suffers from inter- and intra-observer variability. Deep learning techniques could be exploited in order for robust arrhythmia detection models to be designed. In this paper, we propose a novel hybrid neural model utilizing focal loss, an improved version of cross-entropy loss, to deal with training data imbalance. ECG features initially extracted via a Convolutional Neural Network (CNN) are input to a Long Short-Term Memory (LSTM) model for temporal dynamics memorization and thus, more accurate classification into the four ECG rhythm types, namely normal (N), atrial fibrillation (AFIB), atrial flutter (AFL) and AV junctional rhythm (J). The model was trained on the MIT-BIH Atrial Fibrillation Database and achieved a sensitivity of 97.87%, and specificity of 99.29% using a ten-fold cross-validation strategy. The proposed model can aid clinicians to detect common atrial fibrillation in real-time on routine screening ECG. [ABSTRACT FROM AUTHOR]

• A new transfer learning based approach for the classification of breast cancer in histopathological images is proposed. • It can handle both magnification dependent (MI) and independent (MI) binary and eight-class classifications simultaneously. • The residual CNN ResNet-18 is investigated as a backbone model with block-wise fine-tuning strategy. • GCN from the target's task values and three-fold data augmentation are exploited to boost the classification performance. • The proposed approach outperforms 11 recent state-of-the-art MD and MI counterparts by a fair margin. The visual analysis of histopathological images is the gold standard for diagnosing breast cancer, yet a strenuous and an intricate task that requires years of pathologist training. Therefore, automating this task using computer-aided diagnosis (CAD) is highly expected. This paper proposes a new transfer learning-based approach to automated classification of breast cancer from histopathological images, including magnification dependent (MD) and magnification independent (MI) binary and eight-class classifications. We apply the deep neural network ResNet-18 to this problem, which is pre-trained on ImageNet, a large dataset of common images. We then design our transfer learning method to refine the network on histopathological images. Our transfer learning method is based on block-wise fine-tuning strategy; in which we make the last two residual blocks of the deep network model more domain-specific to our target data. It substantially helps to avoid over-fitting and speed up the training. Furthermore, we strengthen the adaptability of the proposed approach by using global contrast normalization (GCN) based on the target's data values and three-fold data augmentation on training data. The experimental results of MD and MI binary and eight-class classifications on the publicly available BreaKHis dataset demonstrate that our approach is promising and effective, outperforming recent state-of-the-art MD and MI counterparts by a fair margin. [ABSTRACT FROM AUTHOR]

• Sample-wise classification of ECG waves from continuous raw noisy signals. • Channel-mix convolutional to handle lead-to lead variabilities. • Hybrid model for automatic extraction and classification of samples by considering forward and backward time stamps. • Experiments with the noisy dataset to increase the robustness, reliability and generalization. • There are 5.77-12.28 % increments in weighted average accuracy using the proposed hybrid model than other LSTM networks. Interpretation of the ECG waves plays a vital role in analysis of cardiovascular diseases. Therefore, many semi and fully-automatic approaches using advanced machine learning techniques for the ECG waves detection are being exhaustively investigated by the researchers. Regardless of advanced machine learning or deep learning techniques, present methods lacks generalization, robustness, reliability and real-time implementation. Moreover, none of the existing study had presented the information related to lead variability which is imperative aspects to handle for accurate detection of ECG waves. Hence, in this paper, we are presenting the new approach of ECG wave segmentation called semantic segmentation, a well-known concept in image segmentation. The proposed approach mainly includes; 1) hybrid channel-mix convolutional and bidirectional LSTM which able to extract temporal dependencies as well as short and long-time dependencies in forward and backward time stamps for semantic segmentation. 2) Experiments with channel-mix convolution to handle lead to lead variability, 3) Experiments with noisy dataset to increase the robustness, reliability and generalization. The proposed hybrid model is implemented on the standard publicly available QT database. The remarkable results with high accuracy ranging from 96 to 98.56 % with average and weighted accuracies of 96.72 % and 95.54 % respectively are obtained for segmentation of ECG waves from continuous raw ECG signal. There are 12.28 %, 8.08 % and 5.77 % increments in weighted average accuracy using proposed hybrid model than LSTM, BiLSTM and double BiLSTM respectively. [ABSTRACT FROM AUTHOR]