This article is mainly concerned with COVID-19 diagnosis from X-ray images. The number of cases infected with COVID-19 is increasing daily, and there is a limitation in the number of test kits needed in hospitals. Therefore, there is an imperative need to implement an efficient automatic diagnosis system to alleviate COVID-19 spreading among people. This article presents a discussion of the utilization of convolutional neural network (CNN) models with different learning strategies for automatic COVID-19 diagnosis. First, we consider the CNN-based transfer learning approach for automatic diagnosis of COVID-19 from X-ray images with different training and testing ratios. Different pre-trained deep learning models in addition to a transfer learning model are considered and compared for the task of COVID-19 detection from X-ray images. Confusion matrices of these studied models are presented and analyzed. Considering the performance results obtained, ResNet models (ResNet18, ResNet50, and ResNet101) provide the highest classification accuracy on the two considered datasets with different training and testing ratios, namely 80/20, 70/30, 60/40, and 50/50. The accuracies obtained using the first dataset with 70/30 training and testing ratio are 97.67%, 98.81%, and 100% for ResNet18, ResNet50, and ResNet101, respectively. For the second dataset, the reported accuracies are 99%, 99.12%, and 99.29% for ResNet18, ResNet50, and ResNet101, respectively. The second approach is the training of a proposed CNN model from scratch. The results confirm that training of the CNN from scratch can lead to the identification of the signs of COVID-19 disease.

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About 50 million people around the world are affected by epilepsy disorders of different kinds. Any person, of any age, gender, race, or class, may be affected by epilepsy. In addition, epilepsy seizures can also vary in frequency of occurrence. Such seizures sometimes cause cognitive disorders, which may lead to physical injury of the patients. 1 Epilepsy is recognized by the World Health Organization (WHO) as a public health concern because of its physical and psychological consequences. Moreover, epilepsy may lead to premature death, loss of work productivity, and increased healthcare needs and expenditure. 2 For diagnosing epileptic seizures, distinct screening techniques have been developed; including Electroencephalography (EEG), positron emission tomography, magneto encephalography, and magnetic resonance imaging. EEG signals are characterized by being easily acquired with portable devices. 3 EEG can be defined as an electrophysiological exploration method by which electrical activities of the brain are measured using electrodes fixed on the scalp. 4 These electrodes may be bulky for patients. Utilization of EEG signals for diagnosing epilepsy is time-and effort-consuming; as epileptologists have to screen EEG signals minute by minute. Furthermore, human error is inevitable. Hence, a computer-based diagnosis, by which epileptic seizures can be early detected, is expected to help the patients. [5][6][7][8][9] Artificial intelligence covers several areas and includes several branches such as Machine Learning (ML) and Deep Learning (DL).Conventional ML algorithms, including feature extraction and classification, were formerly used before the appearance of DL. Hand-crafted features limit the performance of the classification algorithms, but deep features are preferred due to their better representation of signals and images. Such techniques have achieved great progress, when used in many aspects of medicine, especially in the diagnosis of epileptic seizures. In many fields, such as anomaly detection from medical signals and images, feature learning, target monitoring, and recognition; DL has achieved great advances. [10][11][12] In this paper, we propose an efficient strategy for both seizure detection and prediction from medical EEG signals. Three models are presented for the classification task. Two of them are patient-specific, while the third one is patient non-specific. EEG signals for epilepsy patients can be divided into three states: normal (inter-ictal), ictal (seizure), and pre-ictal which represents the period of 30-60 min before the ictal state. 13 We assumed in this paper that the pre-ictal state occurs 30 min before the ictal state. The two-class classification is implemented between normal and pre-ictal activities for seizure prediction and between normal and ictal activities for seizure detection. A more generalized threeclass classification framework is considered to identify all EEG signal activities. For the first two proposed models, the spectrogram estimation process is performed on EEG si...

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The brain tumor is an urgent malignancy caused by unregulated cell division. Tumors are classified using a biopsy, which is normally performed after the final brain surgery. Deep learning technology advancements have assisted the health professionals in medical imaging for the medical diagnosis of several symptoms. In this paper, transfer-learning-based models in addition to a Convolutional Neural Network (CNN) called BRAIN-TUMOR-net trained from scratch are introduced to classify brain magnetic resonance images into tumor or normal cases. A comparison between the pre-trained InceptionResNetv2, Inceptionv3, and ResNet50 models and the proposed BRAIN-TUMOR-net is introduced. The performance of the proposed model is tested on three publicly available Magnetic Resonance Imaging (MRI) datasets. The simulation results show that the BRAIN-TUMOR-net achieves the highest accuracy compared to other models. It achieves 100%, 97%, and 84.78% accuracy levels for three different MRI datasets. In addition, the k-fold cross-validation technique is used to allow robust classification. Moreover, three different unsupervised clustering techniques are utilized for segmentation.

This article focuses on automatic modulation classification (AMC) in wireless communication systems. A convolutional neural network (CNN) with three layers is introduced for the AMC process. Over degraded channels, it is assumed that the constellation diagrams of received signals do not show sharp points as in the case of pure signals. Instead, the points spread to constitute circle‐shaped objects. With more deterioration in channel conditions, these circle‐shaped objects begin to show overlapping. This behavior motivates us to use object detection, when dealing with the modulation classification task. The selection of the adopted transforms in this article is made from the object detection perspective. Different 2D transforms are considered on the constellation diagrams and compared for better classification performance. These transforms are the Radon transform (RT), the curvelet transform, and the phase congruency (PC). They are applied on the 2D constellation diagrams prior to the classification task with the CNN. The classification of the modulation format at different signal‐to‐noise ratios (SNRs) is considered in this article from the constellation diagrams, and the preprocessed constellation diagrams using RT, curvelet transform, and PC. Seven types of modulation formats are considered in this study to represent both spread and dense constellation diagram patterns, and the study extends from −10 to 10 dB. Analysis of the results indicating the most suitable preprocessor according to the constellation type and the SNR involved is provided.