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Epilepsy is a long-term neurological condition that disrupts brain function in people of all ages, epilepsy is a condition that is analysed through the brain signals via electroencephalogram (EEG) signal. To analyse epilepsy using spatial and temporal data, various machine-learning-based techniques are used. However, most of the techniques suffer from inaccuracy issues in dealing with the dynamic and raw EEG signal. In this research, an intelligent societal optimisation-driven classifier is introduced based on convolutional neural networks (CNN) for epileptic seizure prediction using EEG signals. To boost predictive accuracy, we extract frequency band features from the EEG signal utilising wavelet decomposition. The frequency band features form the feature vector, is provided smart societal optimisation- CNN such that the prediction performance is enhanced through the optimal tuning of the CNN with the smart societal optimisation. Smart societal optimisation is proposed by integrating the behaviour of the Lobos wolf and the Moggie. The smart societal optimisation-based CNN attains 87.673% accuracy, 84.949% sensitivity91.274%specificity for the K-Fold-10 for CHB-MIT scalp EEG database. [ABSTRACT FROM AUTHOR]

Deep learning classification is commonly used to assess biomedical data. Fetal heart rate (FHR) signal data monitor the health of both the mother and the fetus and prevent mobility and death in fetuses at risk of hypoxia. But, the data imbalance is a major problem and happens frequently. To overcome this problem, a Deep Siamese domain adaptation convolutional Neural Network optimized with Honey Badger Algorithm (HBA) is proposed in this paper for Fetal Arrhythmia Detection and Classification from ECG signals (DSDACNN-HBA-FAC). The input ECG signals are collected from the fetal ECG dataset. Then, the ECG data are pre-processed using coherence shock filtering (CSF), which eliminates artifacts, like baseline drift, contact noise, muscle artifacts, electrode motion, electromyography artifacts. Using the improved non-subs sampled Shearlet transform (INSST) method, the pre-processed ECG data recover the mean, standard deviation, root mean square, skewness, kurtosis, zero-crossing, autoregressive coefficients, estimated entropy, and Higuchi's fractal dimension. The DSDACNN classifier utilizes the features extracted to categorize the output as normal and arrhythmia. In general, DSDACNN does not agree on any optimization techniques to define the optimum parameters and to ensure accurate classification. Therefore, HBA is employed to optimize the DSDACNN weight parameters. The proposed method is implemented in MATLAB. The performance metrics, like sensitivity, precision, recall, f-measure, specificity, accuracy, computation time, RoC, and error rate are evaluated. The performance of DSDACNN-HBA-FAC achieves 3.101%, 7.12%, 7.73% high accuracy, 51.136%, 59.04%, 44.51% lower computation Time and 2.292%, 5.365%, 1.551% greater AUC compared with the existing techniques, like Fetal Arrhythmia Detection with Classification from ECG Signals: A NonInvasive Method (ANN-FAC), Semi-supervised active transfer learning for fetal ECG arrhythmia identification (DNN-FAC), fetal arrhythmia detection using adaptive single channel electrocardiogram extraction (CNN-FAC), respectively. [ABSTRACT FROM AUTHOR]

Epilepsy is a long-term neurological condition that disrupts brain function in people of all ages, epilepsy is a condition that is analysed through the brain signals via electroencephalogram (EEG) signal. To analyse epilepsy using spatial and temporal data, various machine-learning-based techniques are used. However, most of the techniques suffer from inaccuracy issues in dealing with the dynamic and raw EEG signal. In this research, an intelligent societal optimisation-driven classifier is introduced based on convolutional neural networks (CNN) for epileptic seizure prediction using EEG signals. To boost predictive accuracy, we extract frequency band features from the EEG signal utilising wavelet decomposition. The frequency band features form the feature vector, is provided smart societal optimisation- CNN such that the prediction performance is enhanced through the optimal tuning of the CNN with the smart societal optimisation. Smart societal optimisation is proposed by integrating the behaviour of the Lobos wolf and the Moggie. The smart societal optimisation-based CNN attains 87.673% accuracy, 84.949% sensitivity91.274%specificity for the K-Fold-10 for CHB-MIT scalp EEG database.

We evaluated the diagnostic performance of a deep-learning model (DLM) (CorEx®, Spimed-AI, Paris, France) designed to automatically detect > 50% coronary stenosis on coronary computed tomography angiography (CCTA) images. We studied inter-observer variability as an additional aim. CCTA images obtained before transcatheter aortic valve implantation (TAVI) were assessed by two radiologists and the DLM, and the results were compared to those of invasive coronary angiography (ICA) used as the reference standard. 165 consecutive patients underwent both CCTA and ICA as part of their TAVI work-up. We excluded the 42 (25.5%) patients with a history of stenting or bypass grafting and the 23 (13.9%) patients with low-quality images. We retrospectively subjected the CCTA images from the remaining 100 patients to evaluation by the DLM and compared the DLM and ICA results. All 25 patients with > 50% stenosis by ICA also had > 50% stenosis by DLM evaluation of CCTA: thus, the DLM had 100% sensitivity and 100% negative predictive value. False-positive DLM results were common, yielding a positive predictive value of only 39% (95% CI, 27–51%). Two radiologists with 3 and 25 years' experience, respectively, performed similarly to the DLM in evaluating the CCTA images; thus, accuracy did not differ significantly between each reader and the DLM (p = 0.625 and p = 0.375, respectively). The DLM had 100% negative predictive value for > 50% stenosis and performed similarly to experienced radiologists. This tool may hold promise for identifying the up to one-third of patients who do not require ICA before TAVI. [ABSTRACT FROM AUTHOR]

Skin cancer, a critical health concern, necessitates accurate early detection and classification to mitigate its impact. However, the limited availability of medical datasets and the challenge of optimizing learnable parameters in deep learning strategies for medical images pose significant hurdles. To address these challenges, we propose an innovative solution—a hybrid artificial intelligence (AI) framework for skin cancer prediction. This framework consists of two pivotal steps: firstly, a comprehensive skin cancer dataset is curated by combining diverse public datasets encompassing multiple disease types. This facilitates robust weight tuning of Convolutional Neural Networks (CNNs) by enhancing the diversity of the learning process. Subsequently, the optimization of deep learning is meticulously executed through the artificial Bee Colony (ABC) strategy, effectively mitigating the potential adverse effects of initial parameter randomness on AI model performance. Rigorous evaluation of the hybrid framework against eight prominent CNN models, including DenseNet121, InceptionResNetV2, and others, underscores its superior predictive capabilities. The optimized hybrid framework achieves exceptional results, boasting an accuracy of 93.04%, recall of 92.0%, precision of 93.0%, and an impressive F1-score of 93.12%. In summary, our proposed hybrid solution demonstrates a substantial advancement in multi-disease skin cancer classification. It outperforms existing AI models and holds promise as a pragmatic smart solution with far-reaching implications.

Cyberbullying poses a dangerous threat to teens, children, young people, and adults in today's digital world. Bullies use virtual social media platforms like Twitter, Facebook, YouTube, and Instagram to harm their victims. Therefore, this online bullying (cyberbullying) is now rising as an important societal issue that affects the person emotionally and psychologically. Among the number of studies conducted to deal with cyberbullying detection, most of them are limited to only text-based content. Since the network is continuously growing, the presence of visual and audio based provoking is also needed to be considered. Therefore, the proposed study aims to develop an automatic multi-modal cyberbullying detection on social media platforms through Multi-modal Decision Fusion Classifier. The proposed multi-modal cyberbullying detection steps are data collection, multi-modality generation, score-based fusion and classification. Multiple major modalities, such as audio, visual and textual, are initially gathered from appropriate datasets. The data collected is provided as input to a multi-modal generation, where the modality is generated separately for each input. The text modality is initially generated using the hybrid Bi-directional Long Short-Term Memory assisted Attention Hierarchical Capsule Network (BiLSTM-AHCNet) model. Next, the imaging modality is generated with the Tuned Aquila EfficientB0 (Tuned AEB0) model. Finally, the audio features are extracted through the Librosa library, and the extracted features are fed to the Attention Convolutional Neural Network (ACNN) model for acoustic modality generation. In the proposed work, the Multi-Modality Decision Fusion Classifier (MMDFC) is employed to fuse all modalities, and the classification is performed by adopting the softmax layer. In addition, a weighting scheme based on horse herd optimization is applied in the fusion phase to allow accurate characterization of the features. For the simulation analysis, the proposed study used Python, and the effectiveness of the proposed techniques is analyzed by measuring several performance metrics like accuracy (98.23%), F-measure (98.22%), specificity (98.47%) and AUC (0.982). [ABSTRACT FROM AUTHOR]

Nanomaterial-based aptasensors serve as useful instruments for detecting small biological entities. This work utilizes data gathered from three electrochemical aptamer-based sensors varying in receptors, analytes of interest, and lengths of signals. Our ultimate objective was the automatic detection and quantification of target analytes from a segment of the signal recorded by these sensors. Initially, we proposed a data augmentation method using conditional variational autoencoders to address data scarcity. Secondly, we employed recurrent-based networks for signal extrapolation, ensuring uniform signal lengths. In the third step, we developed seven deep learning classification models (GRU, unidirectional LSTM (ULSTM), bidirectional LSTM (BLSTM), ConvGRU, ConvULSTM, ConvBLSTM, and CNN) to identify and quantify specific analyte concentrations for six distinct classes, ranging from the absence of analyte to 10 μM. Finally, the second classification model was created to distinguish between abnormal and normal data segments, detect the presence or absence of analytes in the sample, and, if detected, identify the specific analyte and quantify its concentration. Evaluating the time series forecasting showed that the GRU-based network outperformed two other ULSTM and BLSTM networks. Regarding classification models, it turned out signal extrapolation was not effective in improving the classification performance. Comparing the role of the network architectures in classification performance, the result showed that hybrid networks, including both convolutional and recurrent layers and CNN networks, achieved 82% to 99% accuracy across all three datasets. Utilizing short-term Fourier transform (STFT) as the preprocessing technique improved the performance of all datasets with accuracies from 84% to 99%. These findings underscore the effectiveness of suitable data preprocessing methods in enhancing neural network performance, enabling automatic analyte identification and quantification from electrochemical aptasensor signals. [ABSTRACT FROM AUTHOR]

Cluster computing is an essential technology in distributed environments for practical data analysis in complex datasets like tumor segmentation, disease classification etc. Today real-world applications like medicine and transport are needed for big data analytics environments. This research article considers complex image data environments like brain tumor segmentation based on advanced clustering techniques for effective tumor prediction. An a-state-of-art analysis used Hierarchical clustering to extract initial tumor segments from the image. The next segment is further refined using novel Noise detection-based levelsetting techniques. The unsupervised Fuzzy C-means and k-means clustering is used to segment the diseases affected region to enhance noise detection used in the level set. Effective features are extracted using gray level co-occurrence matrix and redundant discrete wavelet transform. Finally, classifying malignant and benign brain tumor images is done using deep probabilistic neural networks. Publicly available datasets are used to validate the proposed algorithms. Experimental results prove that proposed pipeline techniques have effective performance in tumor segmentation and classification model. [ABSTRACT FROM AUTHOR]

To improve the accuracy of demand forecasting for new electronic products, especially in scenarios with limited historical data, a novel forecasting model was proposed in this study which integrated K-means based on Euclidian distance, Multi-layer perceptron algorithm, and Quantile Regression with Gradient Boosted Trees (KEM-QRGBT). The model also incorporated grid search with K-fold cross-validation to enable the adaptive selection of the optimal parameters for product data. Additionally, the KEM-QRGBT model, which can balance the intricacies of learning parameter patterns with its ability to quantify demand uncertainty, exhibited proficiency in quantifying the uncertainty inherent in demand forecasting. Using a case study from a manufacturing enterprise in Turkey, the effectiveness of the model was validated. Results demonstrate that, for new electronic products with limited historical data, the KEMQRGBT model with adaptive parameter selection improves demand forecasting accuracy, outperforming benchmark methods, and other machine learning models. The proposed algorithm provides a strong evidence for the demand forecasting of new electronic products, particularly in cases where historical data is limited. [ABSTRACT FROM AUTHOR]

COVID-19 is a deadly virus that first appeared in late 2019 and spread rapidly around the world. Understanding and classifying computed tomography images (CT) is extremely important for the diagnosis of COVID-19. Many case classification studies face many problems, especially unbalanced and insufficient data. For this reason, deep learning methods have a great importance for the diagnosis of COVID-19. Therefore, we had the opportunity to study the architectures of NasNet-Mobile, DenseNet and Nasnet-Mobile+DenseNet with the dataset we have merged. The dataset we have merged for COVID-19 is divided into 3 separate classes: Normal, COVID-19, and Pneumonia. We obtained the accuracy 87.16%, 93.38% and 93.72% for the NasNet-Mobile, DenseNet and NasNet-Mobile+DenseNet architectures for the classification, respectively. The results once again demonstrate the importance of Deep Learning methods for the diagnosis of COVID-19.

Tillage practices alter soil surface structure that can be potentially captured by satellite images with both high spatial and temporal resolution. This study explored tillage practice mapping using the daily Planet Fusion surface reflectance (PF-SR) gap-free 3 m data generated by fusing PlanetScope with Landsat-8, Sentinel-2 and MODIS surface reflectance data. The study area is a 220 × 220 km2 agricultural area in South Dakota, USA, and the study used 3285 PF-SR images from September 1, 2020 to August 31, 2021. The PF-SR images for the surveyed 433 fields were sliced into 10,747 training (70%) and evaluation (30%) non-overlapping time series patches. The training and evaluation patches were from different fields for evaluation data independence. The performance of four deep learning models (i.e., 2D convolutional neural networks (CNN), 3D CNN, CNN-Long short-term memory (LSTM), and attention CNN-LSTM) in tillage practice mapping, as well as their sensitivity to different spatial (i.e., 3 m, 24 m, and 96 m) and temporal resolutions (16-day, 8-day, 4-day, 2-day and 1-day) were examined. Classification accuracy continuously increased with increases in both temporal and spatial resolutions. The optimal models (3D CNN and attention CNN-LSTM) achieved ∼77% accuracy using 2-day or daily 3 m resolution data as opposed to ∼72% accuracy using 16-day 3 m resolution data or daily 24 m resolution data. This study also analyzed the feature importance of different acquisition dates for the two optimal models. The 3D CNN model feature importances were found to agree well with the tillage practice time. High feature importance was associated with observations during the fall and spring tillage period (i.e., fresh tillage signals) whereas the crop peak growing period (i.e., tillage signals weathered and confounded by dense canopy) was characterized by a relatively low feature importance. The work provides valuable insights into the utility of deep learning for tillage mapping and change event time identification based on high resolution imagery.

Plant disease and pest detection machines were originally used in agriculture and have, to some extent, replaced traditional visual identification. Plant diseases and pests are important determinants of plant productivity and quality. Plant diseases and pests can be identified using digital image processing. According to the difference in the structure of the network, this study presents research on the detection of plant diseases and pests based on three aspects of the classification network, detection network, and segmentation network in recent years, and summarizes the advantages and disadvantages of each method. A common data set is introduced and the results of existing studies are compared. This study discusses possible problems in the practical application of plant disease and pest detection based on deep learning. Conventional image processing algorithms or manual descriptive design and classifiers are often used for traditional computer vision-based plant disease and pest detection. This method usually uses various characteristics of plant diseases and pests to create an image layout and selects a useful light source and shooting angle to produce evenly lit images. The purpose of this work is to identify a group of pests and diseases of domestic and garden plants using a mobile application and display the final result on the screen of a mobile device. In this work, data from 38 different classes were used, including diseased and healthy leaf images of 13 plants from plantVillage. In the experiment, Inception v3 tends to consistently improve accuracy with an increasing number of epochs with no sign of overfitting and performance degradation. Keras with Theano backend used to teach architectures [ABSTRACT FROM AUTHOR]

Pine wilt disease (PWD) is responsible for significant damage to East Asia’s pine forests, including those in Korea, Japan, and China. Preventing the spread of wilt disease requires early detection and removal of damaged trees. This paper proposes a method of detecting disease-damaged pines using ortho-images corrected from 5-band multi-spectral images captured by unmanned aviation vehicles. The proposed method relies on a ResNet18 backbone network connected to a modified DenseNet module, classifies the 5-band multispectral (RGB, NIR, Red_Edge) ortho-image patches, and visualizes the results as a heat map. The patch-based classifier was retrained with hard negative examples, after which it achieved 98.66% accuracy, an improvement over the 96.0% accuracy associated with the same method applied to RGB images. The resulting heat map reflects the approximate distribution, and movement of the disease. Disease locations are also predicted by local maximums in the heat map. When the distance between a ground truth and the predicted location is less than visible distance, e.g. about 5m, it is counted as a correct detection. The proposed detection which consists of heat map generation followed by localization achieves Recall of 93.39%, Precision of 88.26%, and F1-score of 90.75%.

The data of impacts and damage caused by floods is necessary for manipulation to assist and relieve those impacts in each area. The main issue for data acquisition was acquisition methods that affect the durations, accuracy, and completeness of data obtained. Most data are currently obtained by field survey for data on impacts in each area. However, this method contains limitations, i.e., taking a long time, high cost, and no real-time data visualization. Thus, this research presented the study to develop an application for inspecting areas under impact and damage caused by floods using deep learning classification for flood classification and land use type classification in the affected areas using digital images, remote sensing data, and crowdsource data notified by users through the accuracy assessment application of classification. It was found that deep learning classification for flood classification had 97.50% accuracy, with Kappa = 0.95. Land use type classification had 93.72% accuracy, with Kappa = 0.91. Flood damage assessment process in this research was different from other previous research that used geospatial data for flood damage inspection. In previous research, there was no platform to provide users with information about the impact and damage caused by floods in each area. Also, the data cannot be visualized in real-time. In contrast, this research brought damage data notified by users for processing with flood data in each area by satellite image processing and land use types of classification. The proposed application can calculate damage in each area and visualize real-time results in maps and graphs on the dashboard via the application. Besides, the presented method can be used to verify and visualize data of areas under impact and damage caused by floods in different areas. [ABSTRACT FROM AUTHOR]

Nowadays, the fashion clothing industry is moving towards "fast" fashion, offering a wide variety of products based on different patterns and styles, usually characterized by lower costs and ambiguous quality. The retails markets are trying to present regularly new fashion collections while trying to follow the latest fashion trends at the same time. The main reason is to remain competitive and keep up with ever-changing customer demands. Fashion designers draw inspiration from social media, e-shops, and fashion shows that set the new fashion trends. In this direction, we propose Science4Fashion, an AI end-to-end system that facilitates fashion designers by collecting and analyzing data from many different sources and suggesting products according to their needs. An overview of the system's modules is presented, emphasizing data collection, data annotation using deep learning models, and product recommendation and user feedback processes. The experiments presented in this paper are twofold: (a) experiments regarding the evaluation of clothing attribute classification, and (b) experiments regarding product recommendation using the baseline kNN enriched by the frequency-based clustering algorithm (FBC), achieving promising results. [ABSTRACT FROM AUTHOR]

The growing popularity of the mini-camera is posing a serious threat to privacy and personal security. Disguised as common tools in rooms, these devices can become undetectable. Moreover, conventional active laser detection systems often fail to recognize them owing to their small lens size, weak reflectivity, and the influence of interference targets. In this paper, a method for building a laser active detection system for mini-cameras is proposed. Using a monostatic optical system and a deep learning classification algorithm, this anti-camera system can detect mini-cameras accurately in real time. This article describes the system components including its optical design, core components and image processing algorithm. The capability of the system for detecting mini-cameras and identifying interference is also experimentally demonstrated. This work successfully overcomes the limit of mini-camera detection using deep learning methods in active laser detection systems.

Breast cancer is the most frequently diagnosed cancer in woman. The correct identification of the HER2 receptor is a matter of major importance when dealing with breast cancer: an over-expression of HER2 is associated with aggressive clinical behaviour; moreover, HER2 targeted therapy results in a significant improvement in the overall survival rate. In this work, we employ a pipeline based on a cascade of deep neural network classifiers and multi-instance learning to detect the presence of HER2 from Haematoxylin-Eosin slides, which partly mimics the pathologist's behaviour by first recognizing cancer and then evaluating HER2. Our results show that the proposed system presents a good overall effectiveness. Furthermore, the system design is prone to further improvements that can be easily deployed in order to increase the effectiveness score. [ABSTRACT FROM AUTHOR]

Objective: To clarify CT diagnostic performance in extranodal extension of cervical lymph node metastases using deep learning classification. Methods: Seven-hundred and three CT images (178 with and 525 without extranodal extension) in 51 patients with cervical lymph node metastases from oral squamous cell carcinoma were enrolled in this study. CT images were cropped to an arbitrary size to include lymph nodes and surrounding tissues. All images were automatically divided into two datasets, assigning 80% as the training dataset and 20% as the testing dataset. The automated selection was repeated five times. Each training dataset was imported to a deep learning training system "DIGITS". Five learning models were created after 300 epochs of the learning process using a neural network "AlexNet". Each testing dataset was applied to each created learning model and resulting five performances were averaged as estimated diagnostic performances. A radiologist measured the minor axis and three radiologists evaluated central necrosis and irregular borders of each lymph node, and the diagnostic performances were obtained. Results: The deep learning accuracy of extranodal extension was 84.0%. The radiologists' accuracies based on minor axis ≥ 11 mm, central necrosis, and irregular borders were 55.7%, 51.1% and 62.6%, respectively. Conclusions: The deep learning diagnostic performance in extranodal extension was significantly higher than that of radiologists. This method is expected to improve diagnostic accuracy by further study with increasing the number of patients. [ABSTRACT FROM AUTHOR]

Objective . During deep-learning-aided (DL-aided) ultrasound (US) diagnosis, US image classification is a foundational task. Due to the existence of serious speckle noise in US images, the performance of DL models may be degraded. Pre-denoising US images before their use in DL models is usually a logical choice. However, our investigation suggests that pre-speckle-denoising is not consistently advantageous. Furthermore, due to the decoupling of speckle denoising from the subsequent DL classification, investing intensive time in parameter tuning is inevitable to attain the optimal denoising parameters for various datasets and DL models. Pre-denoising will also add extra complexity to the classification task and make it no longer end-to-end. Approach . In this work, we propose a multi-scale high-frequency-based feature augmentation (MSHFFA) module that couples feature augmentation and speckle noise suppression with specific DL models, preserving an end-to-end fashion. In MSHFFA, the input US image is first decomposed to multi-scale low-frequency and high-frequency components (LFC and HFC) with discrete wavelet transform. Then, multi-scale augmentation maps are obtained by computing the correlation between LFC and HFC. Last, the original DL model features are augmented with multi-scale augmentation maps. Main results . On two public US datasets, all six renowned DL models exhibited enhanced F1-scores compared with their original versions (by 1.31%-8.17% on the POCUS dataset and 0.46%-3.89% on the BLU dataset) after using the MSHFFA module, with only approximately 1% increase in model parameter count. Significance . The proposed MSHFFA has broad applicability and commendable efficiency and thus can be used to enhance the performance of DL-aided US diagnosis. The codes are available athttps://github.com/ResonWang/MSHFFA., (© 2024 Institute of Physics and Engineering in Medicine.)

• Built a DL method to classify SOT within 6 h to classify patients requiring EVT. • Designed model using MM-UNet to predict the segmentation mask from MR images. • Radiomic features are extracted from the MR images and segmentation masks. • Hidden representations are extracted using the MM-UNet and ResNet-18 models. • Radiomic and deep features are combined to improve classification performance. Clinicians use imaging-based acute stroke onset time (SOT) to make crucial decisions regarding stroke treatments, such as thrombolysis or thrombectomy. Patients may receive intravenous thrombolysis and undergo endovascular thrombectomy (EVT) within 3 or 4.5 h and 6 h from SOT, respectively. Most of the classification algorithms developed so far classify SOT within 4.5 h for thrombolysis. In this study, we demonstrated a deep learning (DL) method to classify SOT within 6 h to identify patients requiring EVT. We developed a DL-based segmentation model using a multi-modal UNet (MM-UNet) to predict the region of interest (ROI) from magnetic resonance (MR) images. Radiomic features were extracted from the MR images and ROI. Additionally, we proposed a DL model to extract hidden representations (deep features) using the MM-UNet and ResNet-18 models. We found that the classification performance improved by combining radiomic and deep features. The cross-validation results indicate that our proposed method sufficiently classified SOT within 6 h, achieving an F0.5 score of 80.6%. The DL model using multi-modal MR images can potentially become a practical decision-support tool for stroke treatments. [ABSTRACT FROM AUTHOR]

Breast cancer is the most frequently diagnosed cancer in woman. The correct identification of the HER2 receptor is a matter of major importance when dealing with breast cancer: an over-expression of HER2 is associated with aggressive clinical behaviour; moreover, HER2 targeted therapy results in a significant improvement in the overall survival rate. In this work, we employ a pipeline based on a cascade of deep neural network classifiers and multi-instance learning to detect the presence of HER2 from Haematoxylin–Eosin slides, which partly mimics the pathologist’s behaviour by first recognizing cancer and then evaluating HER2. Our results show that the proposed system presents a good overall effectiveness. Furthermore, the system design is prone to further improvements that can be easily deployed in order to increase the effectiveness score.

We consider a deep learning model for classifying high-dimensional data and seek to achieve optimal evaluation accuracy and robustness based on multicriteria decision-making (MCDM) for high-dimensional data analysis applications during comprehensive evaluation (CE) activities. We propose a novel one-dimensional visual geometry group network (1D_VGGNet) to overcome the problem that high-dimensional data are too complicated and unstable to be feasibly applied. Then, to effectively handle one-dimensional MCDM, we present a 1D_VGGNet classifier to replace the two-dimensional convolution operation applied to image data with a one-dimensional convolution operation applied to one-dimensional MCDM. Furthermore, to solve the invariance problem of the generated feature maps, the maxpooling kernel size can be flexibly adjusted to effectively meet the requirements of reducing the feature map dimension and speeding up training and prediction on different datasets. The improvement is reasonable for various high-dimensional data application scenarios. Moreover, we propose a novel objective function to accurately evaluate network performance since the objective function includes a variety of representative performance evaluation metrics, and the average value is calculated as one of the CE metrics. The experimental results illustrate that the proposed framework outperforms a one-dimensional convolutional neural network (1D_CNN) for comprehensive classification on the Shaoxing University student achievement dataset and the MIT-BIH Arrhythmia database and achieves average gains of 36.3% and 12.1% in terms of the designated evaluation metric. • Propose 1D_VGGNet framework to classify and evaluate high-dimensional data. • Replace the two-dimensional convolution of 2D_VGGNet by one-dimensional convolution. • Present a novel objective function to evaluate the network model. [ABSTRACT FROM AUTHOR]

Sarcomatoid mesothelioma is an aggressive malignancy that can be challenging to distinguish from benign spindle cell mesothelial proliferations based on biopsy, and this distinction is crucial to patient treatment and prognosis. A novel deep learning based classifier may be able to aid pathologists in making this critical diagnostic distinction. SpindleMesoNET was trained on cases of malignant sarcomatoid mesothelioma and benign spindle cell mesothelial proliferations. Performance was assessed through cross-validation on the training set, on an independent set of challenging cases referred for expert opinion ('referral' test set), and on an externally stained set from outside institutions ('externally stained' test set). SpindleMesoNET predicted the benign or malignant status of cases with AUC's of 0.932, 0.925, and 0.989 on the cross-validation, referral and external test sets, respectively. The accuracy of SpindleMesoNET on the referral set cases (92.5%) was comparable to the average accuracy of 3 experienced pathologists on the same slide set (91.7%). We conclude that SpindleMesoNET can accurately distinguish sarcomatoid mesothelioma from benign spindle cell mesothelial proliferations. A deep learning system of this type holds potential for future use as an ancillary test in diagnostic pathology., (© 2021. The Author(s), under exclusive licence to United States & Canadian Academy of Pathology.)

Coronavirus disease (COVID-19) has been the main agenda of the whole world ever since it came into sight. X-ray imaging is a common and easily accessible tool that has great potential for COVID-19 diagnosis and prognosis. Deep learning techniques can generally provide state-of-the-art performance in many classification tasks when trained properly over large data sets. However, data scarcity can be a crucial obstacle when using them for COVID-19 detection. Alternative approaches such as representation-based classification [collaborative or sparse representation (SR)] might provide satisfactory performance with limited size data sets, but they generally fall short in performance or speed compared to the neural network (NN)-based methods. To address this deficiency, convolution support estimation network (CSEN) has recently been proposed as a bridge between representation-based and NN approaches by providing a noniterative real-time mapping from query sample to ideally SR coefficient support, which is critical information for class decision in representation-based techniques. The main premises of this study can be summarized as follows: 1) A benchmark X-ray data set, namely QaTa-Cov19, containing over 6200 X-ray images is created. The data set covering 462 X-ray images from COVID-19 patients along with three other classes; bacterial pneumonia, viral pneumonia, and normal. 2) The proposed CSEN-based classification scheme equipped with feature extraction from state-of-the-art deep NN solution for X-ray images, CheXNet, achieves over 98% sensitivity and over 95% specificity for COVID-19 recognition directly from raw X-ray images when the average performance of 5-fold cross validation over QaTa-Cov19 data set is calculated. 3) Having such an elegant COVID-19 assistive diagnosis performance, this study further provides evidence that COVID-19 induces a unique pattern in X-rays that can be discriminated with high accuracy.

With the rapid increase of data availability, time series classification (TSC) has arisen in a wide range of fields and drawn great attention of researchers. Recently, hundreds of TSC approaches have been developed, which can be classified into two categories: traditional and deep learning based TSC methods. However, it remains challenging to improve accuracy and model generalization ability. Therefore, we investigate a novel end-to-end model based on deep learning named as Multi-scale Attention Convolutional Neural Network (MACNN) to solve the TSC problem. We first apply the multi-scale convolution to capture different scales of information along the time axis by generating different scales of feature maps. Then an attention mechanism is proposed to enhance useful feature maps and suppress less useful ones by learning the importance of each feature map automatically. MACNN addresses the limitation of single-scale convolution and equal weight feature maps. We conduct a comprehensive evaluation of 85 UCR standard datasets and the experimental results show that our proposed approach achieves the best performance and outperforms the other traditional and deep learning based methods by a large margin., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2021 Elsevier Ltd. All rights reserved.)

This study aimed to develop a deep learning-based image classification model that can differentiate tufted astrocytes (TA), astrocytic plaques (AP), and neuritic plaques (NP) based on images of tissue sections stained with phospho-tau immunohistochemistry. Phospho-tau-immunostained slides from the motor cortex were scanned at 20× magnification. An automated deep learning platform, Google AutoML, was used to create a model for distinguishing TA in progressive supranuclear palsy (PSP) from AP in corticobasal degeneration (CBD) and NP in Alzheimer disease (AD). A total of 1500 images of representative tau lesions were captured from 35 PSP, 27 CBD, and 33 AD patients. Of those, 1332 images were used for training, and 168 images for cross-validation. We tested the model using 100 additional test images taken from 20 patients of each disease. In cross-validation, precision and recall for each individual lesion type were 100% and 98.0% for TA, 98.5% and 98.5% for AP, and 98.0% and 100% for NP, respectively. In a test set, all images of TA and NP were correctly predicted. Only eleven images of AP were predicted to be TA or NP. Our data indicate the potential usefulness of deep learning-based image classification methods to assist in differential diagnosis of tauopathies., (© 2021 American Association of Neuropathologists, Inc. All rights reserved.)

Echo State Networks (ESNs) are efficient recurrent neural networks (RNNs) which have been successfully applied to time series modeling tasks. However, ESNs are unable to capture the history information far from the current time step, since the echo state at the present step of ESNs mostly impacted by the previous one. Thus, ESN may have difficulty in capturing the long-term dependencies of temporal data. In this paper, we propose an end-to-end model named Echo Memory-Augmented Network (EMAN) for time series classification. An EMAN consists of an echo memory-augmented encoder and a multi-scale convolutional learner. First, the time series is fed into the reservoir of an ESN to produce the echo states, which are all collected into an echo memory matrix along with the time steps. After that, we design an echo memory-augmented mechanism employing the sparse learnable attention to the echo memory matrix to obtain the Echo Memory-Augmented Representations (EMARs). In this way, the input time series is encoded into the EMARs with enhancing the temporal memory of the ESN. We then use multi-scale convolutions with the max-over-time pooling to extract the most discriminative features from the EMARs. Finally, a fully-connected layer and a softmax layer calculate the probability distribution on categories. Experiments conducted on extensive time series datasets show that EMAN is state-of-the-art compared to existing time series classification methods. The visualization analysis also demonstrates the effectiveness of enhancing the temporal memory of the ESN., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2020 Elsevier Ltd. All rights reserved.)

Purpose: We sought to assess the performance of deep learning approaches for differentiating nonglaucomatous optic neuropathy with disc pallor (NGON) vs glaucomatous optic neuropathy (GON) on color fundus photographs by the use of image recognition., Design: Development of an Artificial Intelligence Classification algorithm., Methods: This single-institution analysis included 3815 fundus images from the picture archiving and communication system of Seoul National University Bundang Hospital consisting of 2883 normal optic disc images, 446 NGON images, and 486 GON images. The presence of NGON and GON was interpreted by 2 expert neuro-ophthalmologists and had corroborated evidence on visual field testing and optical coherence tomography. Images were preprocessed in size and color enhancement before input. We applied the convolutional neural network (CNN) of ResNet-50 architecture. The area under the precision-recall curve (average precision) was evaluated for the efficacy of deep learning algorithms to assess the performance of classifying NGON and GON., Results: The diagnostic accuracy of the ResNet-50 model to detect GON among NGON images showed a sensitivity of 93.4% and specificity of 81.8%. The area under the precision-recall curve for differentiating NGON vs GON showed an average precision value of 0.874. False positive cases were found with extensive areas of peripapillary atrophy and tilted optic discs., Conclusion: Artificial intelligence-based deep learning algorithms for detecting optic disc diseases showed excellent performance in differentiating NGON and GON on color fundus photographs, necessitating further research for clinical application., (Copyright © 2020 Elsevier Inc. All rights reserved.)

In the postgenome era, many problems in bioinformatics have arisen due to the generation of large amounts of imbalanced data. In particular, the computational classification of precursor microRNA (pre-miRNA) involves a high imbalance in the classes. For this task, a classifier is trained to identify RNA sequences having the highest chance of being miRNA precursors. The big issue is that well-known pre-miRNAs are usually just a few in comparison to the hundreds of thousands of candidate sequences in a genome, which results in highly imbalanced data. This imbalance has a strong influence on most standard classifiers and, if not properly addressed, the classifier is not able to work properly in a real-life scenario. This work provides a comparative assessment of recent deep neural architectures for dealing with the large imbalanced data issue in the classification of pre-miRNAs. We present and analyze recent architectures in a benchmark framework with genomes of animals and plants, with increasing imbalance ratios up to 1:2000. We also propose a new graphical way for comparing classifiers performance in the context of high-class imbalance. The comparative results obtained show that, at a very high imbalance, deep belief neural networks can provide the best performance.

The multispike tempotron (MST) is a powersul, single spiking neuron model that can solve complex supervised classification tasks. It is also internally complex, computationally expensive to evaluate, and unsuitable for neuromorphic hardware. Here we aim to understand whether it is possible to simplify the MST model while retaining its ability to learn and process information. To this end, we introduce a family of generalized neuron models (GNMs) that are a special case of the spike response model and much simpler and cheaper to simulate than the MST. We find that over a wide range of parameters, the GNM can learn at least as well as the MST does. We identify the temporal autocorrelation of the membrane potential as the most important ingredient of the GNM that enables it to classify multiple spatiotemporal patterns. We also interpret the GNM as a chemical system, thus conceptually bridging computation by neural networks with molecular information processing. We conclude the letter by proposing alternative training approaches for the GNM, including error trace learning and error backpropagation.

PRéCIS:: A spectral-domain optical coherence tomography (SD-OCT) based deep learning system detected glaucomatous structural change with high sensitivity and specificity. It outperformed the clinical diagnostic parameters in discriminating glaucomatous eyes from healthy eyes., Purpose: The purpose of this study was to assess the performance of a deep learning classifier for the detection of glaucomatous change based on SD-OCT., Methods: Three hundred fifty image sets of ganglion cell-inner plexiform layer (GCIPL) and retinal nerve fiber layer (RNFL) SD-OCT for 86 glaucomatous eyes and 307 SD-OCT image sets of 196 healthy participants were recruited and split into training (197 eyes) and test (85 eyes) datasets based on a patient-wise split. The bottleneck features extracted from the GCIPL thickness map, GCIPL deviation map, RNFL thickness map, and RNFL deviation map were used as predictors for the deep learning classifier. The area under the receiver operating characteristic curve (AUC) was calculated and compared with those of conventional glaucoma diagnostic parameters including SD-OCT thickness profile and standard automated perimetry (SAP) to evaluate the accuracy of discrimination for each algorithm., Results: In the test dataset, this deep learning system achieved an AUC of 0.990 [95% confidence interval (CI), 0.975-1.000] with a sensitivity of 94.7% and a specificity of 100.0%, which was significantly larger than the AUCs with all of the optical coherence tomography and SAP parameters: 0.949 (95% CI, 0.921-0.976) with average GCIPL thickness (P=0.006), 0.938 (95% CI, 0.905-0.971) with average RNFL thickness (P=0.003), and 0.889 (0.844-0.934) with mean deviation of SAP (P<0.001; DeLong test)., Conclusion: An SD-OCT-based deep learning system can detect glaucomatous structural change with high sensitivity and specificity.

Objective: Learning the structures and unknown correlations of a motor imagery electroencephalogram (MI-EEG) signal is important for its classification. It is also a major challenge to obtain good classification accuracy from the increased number of classes and increased variability from different people. In this study, a four-class MI task is investigated., Approach: An end-to-end novel hybrid deep learning scheme is developed to decode the MI task from EEG data. The proposed algorithm consists of two parts: a. A one-versus-rest filter bank common spatial pattern is adopted to preprocess and pre-extract the features of the four-class MI signal. b. A hybrid deep network based on the convolutional neural network and long-term short-term memory network is proposed to extract and learn the spatial and temporal features of the MI signal simultaneously., Main Results: The main contribution of this paper is to propose a hybrid deep network framework to improve the classification accuracy of the four-class MI-EEG signal. The hybrid deep network is a subject-independent shared neural network, which means it can be trained by using the training data from all subjects to form one model., Significance: The classification performance obtained by the proposed algorithm on brain-computer interface (BCI) competition IV dataset 2a in terms of accuracy is 83% and Cohen's kappa value is 0.80. Finally, the shared hybrid deep network is evaluated by every subject respectively, and the experimental results illustrate that the shared neural network has satisfactory accuracy. Thus, the proposed algorithm could be of great interest for real-life BCIs.

Multimodal emotion understanding enables AI systems to interpret human emotions. With accelerated video surge, emotion understanding remains challenging due to inherent data ambiguity and diversity of video content. Although deep learning has made a considerable progress in big data feature learning, they are viewed as deterministic models used in a "black-box" manner which does not have capabilities to represent inherent ambiguities with data. Since the possibility theory of fuzzy logic focuses on knowledge representation and reasoning under uncertainty, we intend to incorporate the concepts of fuzzy logic into deep learning framework. This paper presents a novel convolutional neuro-fuzzy network, which is an integration of convolutional neural networks in fuzzy logic domain to extract high-level emotion features from text, audio, and visual modalities. The feature sets extracted by fuzzy convolutional layers are compared with those of convolutional neural networks at the same level using t-distributed Stochastic Neighbor Embedding. This paper demonstrates a multimodal emotion understanding framework with an adaptive neural fuzzy inference system that can generate new rules to classify emotions. For emotion understanding of movie clips, we concatenate audio, visual, and text features extracted using the proposed convolutional neuro-fuzzy network to train adaptive neural fuzzy inference system. In this paper, we go one step further to explain how deep learning arrives at a conclusion that can guide us to an interpretable AI. To identify which visual/text/audio aspects are important for emotion understanding, we use direct linear non-Gaussian additive model to explain the relevance in terms of causal relationships between features of deep hidden layers. The critical features extracted are input to the proposed multimodal framework to achieve higher accuracy., (Copyright © 2019 Elsevier Ltd. All rights reserved.)

Objective: Electroencephalography (EEG) analysis has been an important tool in neuroscience with applications in neuroscience, neural engineering (e.g. Brain-computer interfaces, BCI's), and even commercial applications. Many of the analytical tools used in EEG studies have used machine learning to uncover relevant information for neural classification and neuroimaging. Recently, the availability of large EEG data sets and advances in machine learning have both led to the deployment of deep learning architectures, especially in the analysis of EEG signals and in understanding the information it may contain for brain functionality. The robust automatic classification of these signals is an important step towards making the use of EEG more practical in many applications and less reliant on trained professionals. Towards this goal, a systematic review of the literature on deep learning applications to EEG classification was performed to address the following critical questions: (1) Which EEG classification tasks have been explored with deep learning? (2) What input formulations have been used for training the deep networks? (3) Are there specific deep learning network structures suitable for specific types of tasks?, Approach: A systematic literature review of EEG classification using deep learning was performed on Web of Science and PubMed databases, resulting in 90 identified studies. Those studies were analyzed based on type of task, EEG preprocessing methods, input type, and deep learning architecture., Main Results: For EEG classification tasks, convolutional neural networks, recurrent neural networks, deep belief networks outperform stacked auto-encoders and multi-layer perceptron neural networks in classification accuracy. The tasks that used deep learning fell into five general groups: emotion recognition, motor imagery, mental workload, seizure detection, event related potential detection, and sleep scoring. For each type of task, we describe the specific input formulation, major characteristics, and end classifier recommendations found through this review., Significance: This review summarizes the current practices and performance outcomes in the use of deep learning for EEG classification. Practical suggestions on the selection of many hyperparameters are provided in the hope that they will promote or guide the deployment of deep learning to EEG datasets in future research.

Hierarchical processing is pervasive in the brain, but its computational significance for learning under uncertainty is disputed. On the one hand, hierarchical models provide an optimal framework and are becoming increasingly popular to study cognition. On the other hand, non-hierarchical (flat) models remain influential and can learn efficiently, even in uncertain and changing environments. Here, we show that previously proposed hallmarks of hierarchical learning, which relied on reports of learned quantities or choices in simple experiments, are insufficient to categorically distinguish hierarchical from flat models. Instead, we present a novel test which leverages a more complex task, whose hierarchical structure allows generalization between different statistics tracked in parallel. We use reports of confidence to quantitatively and qualitatively arbitrate between the two accounts of learning. Our results support the hierarchical learning framework, and demonstrate how confidence can be a useful metric in learning theory., Competing Interests: The authors have declared that no competing interests exist.

We built and validated a deep learning algorithm predicting the individual diagnosis of Alzheimer's disease (AD) and mild cognitive impairment who will convert to AD (c-MCI) based on a single cross-sectional brain structural MRI scan. Convolutional neural networks (CNNs) were applied on 3D T1-weighted images from ADNI and subjects recruited at our Institute (407 healthy controls [HC], 418 AD, 280 c-MCI, 533 stable MCI [s-MCI]). CNN performance was tested in distinguishing AD, c-MCI and s-MCI. High levels of accuracy were achieved in all the classifications, with the highest rates achieved in the AD vs HC classification tests using both the ADNI dataset only (99%) and the combined ADNI + non-ADNI dataset (98%). CNNs discriminated c-MCI from s-MCI patients with an accuracy up to 75% and no difference between ADNI and non-ADNI images. CNNs provide a powerful tool for the automatic individual patient diagnosis along the AD continuum. Our method performed well without any prior feature engineering and regardless the variability of imaging protocols and scanners, demonstrating that it is exploitable by not-trained operators and likely to be generalizable to unseen patient data. CNNs may accelerate the adoption of structural MRI in routine practice to help assessment and management of patients., (Copyright © 2018 The Authors. Published by Elsevier Inc. All rights reserved.)

In this paper, the effectiveness of deep learning for automatic classification of grouper species by their vocalizations has been investigated. In the proposed approach, wavelet denoising is used to reduce ambient ocean noise, and a deep neural network is then used to classify sounds generated by different species of groupers. Experimental results for four species of groupers show that the proposed approach achieves a classification accuracy of around 90% or above in all of the tested cases, a result that is significantly better than the one obtained by a previously reported method for automatic classification of grouper calls.