Search

Correct identification of tumor in brain images is critical for treatment. In the medical domain, class distributions of recorded data could differ with locations and require high levels of privacy while collaboratively training the deep learning (DL) models for classifications. The main aim of this paper is to propose a privacy‐preserving collaborative model for the classification of brain tumor in heterogeneously distributed magnetic resonance imaging (MRI) images. In this paper, initially, an open‐source dataset has been acquired and analyzed as per the required competencies. The acquired dataset has four types of MRI images: pituitary tumor, meningioma tumor, glioma tumor, and no tumor. First, the acquired dataset was analyzed using DL and transfer learning algorithms. By applying implementations of basic algorithms, better algorithms were identified for further implementations in a federated learning ecosystem. DenseNet201‐based transfer learning was identified as a better neural network and further utilized for collaborative transfer learning implementations. Here, the paper also focused on developing a suitable system for a heterogeneous distributed tumor database. Heterogeneous data were converted from the available data by applying nonidentical data distribution. The study discovered that the federated DL models, involving multiple clients, exhibited superior performance compared to conventional pretrained models. The proposed framework possesses distinctive characteristics that distinguish it from existing classification methods for brain tumor identification, particularly in terms of ensuring data privacy for edge devices with limited resources. Due to these additional features, the framework stands as the optimal alternative solution for early diagnosis of brain tumor. [ABSTRACT FROM AUTHOR]

Sleep affects the functioning of all biological processes in the human body. Sleep disorders disrupt the well‐being of an individual and can potentially lead to other health complications. It affects a large part of the population, and their timely and efficient detection is crucial for improving the quality of health. Sleep disorders are generally analyzed using polysomnogram (PSG) signals, which must be captured in sophisticated laboratory settings. However, electrocardiogram (ECG) signals provide information about the electrical activity of the heart, which is strongly linked with disturbances in sleep and can be used as an indicator of disordered sleep. Along with that, the recording of ECG signals is much easier than other signals and can be done remotely without causing any significant disturbance to the subject. This brings practicality to the systems that are already being employed for sleep disorder detection. In this paper, we present a method for identifying types of sleep disorders using ECG signals. The objective of this paper is to investigate the use of ECG signals for the automated identification of insomnia, narcolepsy, periodic leg movement (PLM), rapid eye movement (REM) behavior disorder (RBD), and nocturnal frontal lobe epilepsy (NFLE) against healthy subjects. We aim to develop a machine learning‐based algorithm that automatically classifies ECG signals into these sleep disorder categories. The cyclic alternating pattern (CAP) sleep database was used in this study, which contains PSG recordings from individuals with and without sleep disorders, including insomnia, narcolepsy, PLM, RBD, and NFLE. A wavelet scattering network has been used to extract features from the ECG signals. Various classifiers were tested, and the ensemble bag of trees classifier provided the optimum performance. An overall accuracy of 98% was obtained for the identification of sleep disorders, along with a classification accuracy of 99.37%, 99.45%, 99.23%, 99.4%, and 99.65% for insomnia, narcolepsy, NFLE, PLM, and RBD, respectively, for binary classification against healthy subject data. Our results show that wavelet scattering network and ensemble of bagged tree (EbagT) classification can accurately identify various sleep disorders. They provided evidence that ECG signals can be used to identify and diagnose sleep disorders, which could lead to the development of accurate detection of these sleep disorders. Also, our study demonstrated the effectiveness of the proposed algorithm in identifying multiple sleep disorders, which could lead to more efficient and cost‐effective diagnosis and treatment of sleep disorders. [ABSTRACT FROM AUTHOR]

Parkinson's Disease (PD) is a brain‐related disease that eventually causes disability and disrupts a person's normal life. Most physicians and researchers try to diagnose it quickly and treat it on time. In recent years, computer science and the field of artificial intelligence (machine learning) have helped researchers find a way to detect the disease early. This article proposes a method that diagnoses Parkinson's disease by analyzing the hand drawing shaped by the individuals using Bi‐Directional Long Short‐Term Memory (Bi‐LSTM) neural network. In addition, this paper proposes a Fuzzy Inferential Classifier for the Dense layer, which classifies the output of LSTM Blocks to the associated classes by modifying the Soft‐max function. Our decision to propose this classifier was because, in some cases, hand‐drawing data related to people with Parkinson's disease have no significant difference with the healthy subjects for the distinguishing and are often very similar to each other. A standard dataset has been used in this paper, which includes spirals drawn test (including static spiral, dynamic spiral, and stability specific point) by a group of healthy people and people with Parkinson's disease. The proposed method has reached 97%, 98.5%, and 100% accuracy rates for the three mentioned spiral tests with a smoother training loss and accuracy plots. In addition, the results outperform state‐of‐the‐art research conducted on this dataset and show at least more than 2.5% improvement in the accuracy rate in comparison. [ABSTRACT FROM AUTHOR]

This paper presents a two‐stage deep learning framework, RR‐HCL‐SVM, designed to aid in the assessment of residual thyroid tissues following thyroidectomy, utilizing single‐photon emission computed tomography (SPECT) images. Leveraging the power of deep learning, our model offers a comprehensive solution for the detection and assessment of remaining thyroid tissues. To enhance accuracy, we introduce a unique combination of features, incorporating the Radio Scan Index (RSI) and radiomics features. These features not only improve accuracy but also provide valuable insights into tissue characterization. Moreover, we employ efficient clustering techniques for feature dimension reduction, preserving model performance while reducing computational complexity. Experimental results demonstrate the effectiveness of our approach, achieving an impressive F1 score of 0.97, sensitivity of 0.96, and specificity of 0.98. The RR‐HCL SVM framework holds great promise in the clinical setting for the precise evaluation of residual thyroid tissues post‐thyroidectomy, offering potential benefits for patient care and treatment planning. [ABSTRACT FROM AUTHOR]

Skin cancer is a common type of malignant tumor that poses a serious threat to patients' lives and health, especially melanoma. It may spread to other body parts, resulting in serious complications and death. In the medical field, accurate identification of skin lesion images is crucial for diagnosing different diseases. However, due to the similarity between different skin lesions, it brings some challenges to medical diagnosis. In this paper, a novel Ensemble Learning Model (EL‐DLOA) based on deep learning and optimization algorithms is proposed, which uses four different deep neural network architectures to generate confidence levels for classes, and optimization algorithms are used to integrate these confidence levels to make the final predictions. To ensure the model's accuracy and reliability, it is first trained using three different learning rates to find the best classification performance of the model. Then, a new search method based on the grey wolf optimization algorithm is proposed to enhance the grey wolf search efficiency. The method improves the search mechanism by changing the grey wolf's individual position through random perturbation or adaptive mutation, which solves the problem that the grey wolf algorithm is easy to fall into local optimum. Finally, four different ensemble strategies are used to reduce individual model bias in the classification process. The proposed model is trained and evaluated using the publicly available dataset HAM10000. The experimental results show that the improved grey wolf optimization algorithm effectively avoids the premature convergence problem and improves the search combination efficiency. Furthermore, in the ensemble methods, the adaptive weight average ensemble strategy effectively improves the classification performance, yielding accuracy, precision, recall, and F1 scores of 0.888, 0.837, 0.897, and 0.862, respectively. These metrics show varying degrees of improvement over the best performing single model. In general, the results indicate that the proposed method achieves high accuracy and practicality in skin lesion classification. Our model shows excellent performance in comparison with other existing models, which makes it significant for research and application in dermatology diagnosis. [ABSTRACT FROM AUTHOR]

This paper's primary goal is to diagnose COVID‐19 contamination based on the artificial intelligence approach automatically. We used convolutional neural network deep learning algorithm for analyzing the ECG images to detect cardiac abnormalities, consequent of the contamination by the SARS‐CoV‐2 virus, responsible for the COVID‐19 epidemic. We designed, trained, and evaluated the performance of two deep learning models (MobileNetV2 and VGG16) in detecting and distinguishing between two different classes (healthy subjects and COVID‐19 positive cases). Indeed, this virus attacks the human respiratory system, which could affect the heart system. Thus, developing a deep learning model could help for a quick and efficient diagnosis, prediction, and physician decision‐making. The performed deep learning model will be used for predicting abnormal cardiac activities consequent to the contamination by the virus. The overall classification rate achieved by the models was 99.34% and 99.67% for MobileNetV2 and VGG16, respectively. Therefore, this approach can efficiently contribute to the diagnosis of COVID‐19 contamination. [ABSTRACT FROM AUTHOR]

For the effective diagnosis of cardio vascular disease (CVD), anatomical characteristics of the heart must be examined, which depends on segmenting the cardiac tissues of interest and then classifying them into appropriate pathological groups. In recent years, deep learning (DL)‐based computer aided design (CAD) segmentation has been employed to automate the segmentation process. Despite the evolution of several DL methods, they still fail due to the shape variation of the heart in patients and the availability of a limited amount of data. This paper proposes an effective Saliency and Active Contour‐based Attention UNet3+ algorithm to segment the ventricles of the heart, which is a challenging task for most researchers, especially with an irregularly shaped right ventricle (RV) that varies over cardiac phases. The algorithm outperforms other state‐of‐the‐art methods in DC metrics, which proves its efficiency in automating the segmentation process. [ABSTRACT FROM AUTHOR]

In the last decade, there has been a significant increase in medical cases involving brain tumors. Brain tumor is the tenth most common type of tumor, affecting millions of people. However, if it is detected early, the cure rate can increase. Computer vision researchers are working to develop sophisticated techniques for detecting and classifying brain tumors. MRI scans are primarily used for tumor analysis. We proposed an automated system for brain tumor detection and classification using a saliency map and deep learning feature optimization in this paper. The proposed framework was implemented in stages. In the initial phase of the proposed framework, a fusion‐based contrast enhancement technique is proposed. In the following phase, a tumor segmentation technique based on saliency maps is proposed, which is then mapped on original images based on active contour. Following that, a pre‐trained CNN model named EfficientNetB0 is fine‐tuned and trained in two ways: on enhanced images and on tumor localization images. Deep transfer learning is used to train both models, and features are extracted from the average pooling layer. The deep learning features are then fused using an improved fusion approach known as Entropy Serial Fusion. The best features are chosen in the final step using an improved dragonfly optimization algorithm. Finally, the best features are classified using an extreme learning machine (ELM). The experimental process is conducted on three publically available datasets and achieved an improved accuracy of 95.14, 94.89, and 95.94%, respectively. The comparison with several neural nets shows the improvement of proposed framework. [ABSTRACT FROM AUTHOR]

Large bowel obstruction (LBO) occurs when there is a blockage or twisting in the large bowel that prevents wastes and gas from passing through. If left untreated, the blockage cuts off blood supply to the colon, causing sections of it to die which results in high rates of morbidity and fatality. The examination of clinical symptoms of LBO involves careful inspection of the cecum and colon. Radiologists use X‐rays to inspect the clinical signs. Some research has been done to automate the detection of related abdominal and intestinal diseases. However, all these studies concentrate only on detecting Crohn's, ulcerative colitis, Acute Appendicitis, colorectal cancer, celiac diseases, liver diseases, and chronic kidney diseases. Automatic detection and classification of LBO has not been given due attention so far to the best of the authors knowledge. To address this challenge, we have designed a model for the detection and classification of LBO. The models development comprises of stages such as preprocessing, detection, segmentation, feature extraction, and classification. We used YOLOv3 for detection and used a gray scale level co‐occurrence matrix (GLCM), and a convolutional neural network for feature extraction, while support vector machine (SVM) and softmax were used for classification. The proposed model achieved a diagnostic accuracy of 89% when feature extraction methods such as CNN and median filter with softmax classifier were used. CNN and Gaussian filter with soft max classifier achieved 91%, while CNN and anisotropic filter with soft max classifier achieved 92%. GLCM with threshold segmentation and Gaussian filter with SVM classifier achieved 87%, while CNN with watershed segmentation and Gaussian filter with SVM classifier achieved 97% and CNN‐GLCM with watershed segmentation and anisotropic diffusion filter with SVM classifier achieved 98% for detection and classification of LBO. Finally, this paper presented a performance analysis of various machine learning approaches for detection and classification of LBO. Hence, our model is designed to assist human experts (Radiologists) in diagnosing LBO. [ABSTRACT FROM AUTHOR]

The number of people dying from lung cancer in the world is increasing day by day. Therefore, early diagnosis of lung cancer holds a prominent position for the recovery of patients. Chest computed tomography scan images that are clinical adjuncts are used in the diagnosis of lung cancer. Herein, the proposed method automatically diagnoses lung cancer using machine learning algorithms, probabilistic majority voting, and optimization techniques. The paper uses images from Adenocarcinoma Left Lower Lobe, Large Cell Carcinoma Left Hilum, Normal, and Squamous Cell Carcinoma Left Hilum subjects. 80% and 20% of these images are used in the training and validation set, respectively, and there are 1000 images in the data set. For each image used in the study, image features are extracted using the Bag of Features method (2000 features). Linear Discriminant, Optimizable Support Vector Machine, and Optimizable K‐Nearest Neighbor (KNN) classifiers are trained using these features. Afterward, the new image label is estimated in two steps. First, if the two or three classifier outputs are the same, the image label is detected using the majority voting technique. Second, if the classifier outputs are not the same, the image label is determined according to the most successful classifier, Optimizable KNN. To better experience the majority voting technique used in the proposed method, its detailed theoretical framework is given in the study. Additionally, the Graphical User Interface application of the proposed method has been designed to save time and reduce the workload of expert radiologists. From the results, it is seen that the Accuracy (%), Error (%), Sensitivity (%), Specificity (%), Precision (%), F1 Score (%), Matthews Correlation Coefficient, and Kappa of the proposed method are equal to 99.2837, 0.007163, 99.2571, 99.9139, 99.3486, 99.2816, 0.98738, and 0.94965 respectively. [ABSTRACT FROM AUTHOR]

In the automated diagnosis of breast cancer (BC), microscopic images based on multi‐classification play a prominent role. Multi‐classification of BC means to differentiate among the sub‐categories of BC (papillary carcinoma, ductal carcinoma, fibroadenoma, etc.). However, unpretentious contrasts in various sub‐categories of BC occur due to the wide fluctuation of 1) excessive coherency of malignant cells, 2) high definition image appearance, and 3) excessive heterogeneity in color distribution, which makes the task more crucial. Therefore, the automated sub‐category discrimination using microscopic images has great medical diagnostic significance yet has not much explored. Thus, the present paper proposes a framework based on machine learning (ML) and deep learning (DL) to multi‐classify BC cells into 8 sub‐categories. These 8 sub‐categories comprise four kinds that delineate benigncy, and the other four portray malignancy. More appropriately, both the ML and DL models with the concept of transfer learning have been proposed as DeepML framework to achieve multi‐classification of BC using histopathological images. The DeepML framework has achieved distinguished performance (approx. 98% & 89% average accuracy for 90–10% and 80–20% train‐test split, respectively) on a wide scale dataset, which intimate the quality of the proposed framework among existing approaches. [ABSTRACT FROM AUTHOR]

In recent years, early detection of hepatitis C virus (HCV) disease has been a vital task in the medical science field. HCV became the main health concern to the public, as it was noticed to have more blood donors in Egypt equated to other nationalities. The WHO assessed that in 2019, around 290 000 individuals died from hepatitis C, which says the seriousness of the HCV disease. So, early prediction, preventions, and curing the disease are vital components to save individuals from HCV. In this paper, we propose and investigate experimental results of the five machine learning (ML) models and probabilistic neural network (PNN) based approach to detect the HCV utilizing University of Califonia Irvine (UCI) ML Egyptian HCV dataset. We also analyze the statistical reports of HCV stages and their features. As per result analysis, the random forest (RF) ML model performs superiorly to other traditional ML algorithms with 97.5% of accuracy. The PNN (incremental hidden layer [HL] neurons) based proposal model shows a very high performance (99.6% of accuracy) at 30‐HL neurons of PNN. As per comparative analysis, the proposed model is superior to experimental basic ML models, and early HCV disease works are related to this area. This research focuses on early detection, prevention, and challenges of handling HCV. [ABSTRACT FROM AUTHOR]

Alzheimer's disease (AD), a neurodegenerative disorder, is a form of dementia. Quick or early diagnosis of AD is essential, but most of the available studies have focused on clinical or survey‐based data, leading to data inconsistency as many people feel hesitant or hide information due to disease and societal stigma. Nowadays, current computer‐aided support and techniques are mainly based on feature extraction, but due to redundant or similarly extracted features, any specific model is not producing best performance. This paper proposes a novel mutual relationship–based feature selection model with high‐altitude acute response like features for the brain‐predefined feature areas using magnetic resonance imaging. The mutual relationship between features is used to avoid similar features or create discrimination between different features due to which accuracy gets affected. In this work, four machine learning classifiers, namely decision tree, support vector machine (SVM), k‐nearest neighbor, and Naive Bayes are used for developing predictive model and algorithms. To avoid overfitting in model selection, we have used a 10‐fold cross‐validation for model training and testing. We collected data set from Alzheimer's disease Neuroimaging Initiative repository. The performance of supervised classifiers was estimated with area under the receiver operating characteristic curve, accuracy, precision, recall, and F1 score assessment measures. The results indicate SVM to be the best performing classifier for prediction (SVM recording the highest average area under curve score of 0.936, accuracy of 94.1%, precision of 96.9%, recall of 96.6%, and F1‐score of 96.8%) with 37 discriminant features. [ABSTRACT FROM AUTHOR]

The cataract is the most common cause of severe vision impairment or blindness worldwide. A periodical diagnosis is recommended in order to prevent cataract severity, where screening might be feasibly ensured through fundus images. In this paper, we propose a CAD system to efficiently grade the cataract from fundus images. For such a need, an ensemble learning framework is put forward where the knowledge of three convolutional deep neural networks is stacked in order to perform an efficient cataract prediction. The main contributions of this work are given as follows: (1) Preprocessing and data augmentation of fundus images are carried out to ensure the robustness of the cataract grading; (2) The well‐known DL architectures (Inception‐V3, MobileNet‐V2 and NasNet‐Mobile) are fine‐tuned and learned as base classifiers; (3) An Extreme Learning Machine (ELM) is performed as a meta classifier to stack the predictions of base classifiers. The evaluation is conducted using a dataset of 590 fundus images selected from two public databases. The suggested framework achieves 94.07% accuracy, 94.37% sensitivity, 93.62% specificity, 95.71% precision and 95.04% F‐measure for cataract grading. The experimentation proves that the proposed framework successfully grades fundus images into cataract severity. Moreover, stacking ensemble learning allows achieving a performance that significantly surpasses the ones realized by each DL architecture, applied separately. [ABSTRACT FROM AUTHOR]