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.

Brain tumor is an acute cancerous disease that results from abnormal and uncontrollable cell division. Brain tumors are classified via biopsy, which is not normally done before the brain ultimate surgery. Recent advances and improvements in deep learning technology helped the health industry in getting accurate disease diagnosis. In this paper, a Convolutional Neural Network (CNN) is adopted with image pre-processing to classify brain Magnetic Resonance (MR) images into four classes: glioma tumor, meningioma tumor, pituitary tumor and normal patients, is provided. We use a transfer learning model, a CNN-based model that is designed from scratch, a pre-trained inceptionresnetv2 model and a pre-trained inceptionv3 model. The performance of the four proposed models is tested using evaluation metrics including accuracy, sensitivity, specificity, precision, F1_score, Matthew’s correlation coefficient, error, kappa and false positive rate. The obtained results show that the two proposed models are very effective in achieving accuracies of 93.15% and 91.24% for the transfer learning model and BRAIN-TUMOR-net based on CNN, respectively. The inceptionresnetv2 model achieves an accuracy of 86.80% and the inceptionv3 model achieves an accuracy of 85.34%. Practical implementation of the proposed models is presented.

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Brain tumor is an acute cancerous disease that results from abnormal and uncontrollable cell division. Brain tumors are classified via biopsy, which is not normally done before the brain ultimate surgery. Recent advances and improvements in deep learning (DL) models helped the health industry in getting accurate diseases diagnosis. This article concentrates on the classification of magnetic resonance (MR) images.The objective is to differentiate between glioma tumors, meningioma tumors, pituitary tumors, and normal cases. Four deep convolutional neural networks are considered and compared in this article. These networks are inceptionresnetv2, inceptionv3, transfer learning, and BRAIN-TUMOR-net. A transfer-learning strategy is considered to enhance the performance of pre-trained models and save the time of training. The used dataset is the brain tumor magnetic resonance imaging dataset. It contains four classes including 826 MR images for glioma tumor, 822 MR images for meningioma tumor, 827 MR images for pituitary tumor, and 835 MR images for normal cases. Due to the limited number of images, we use the augmentation strategy to enlarge the size of the dataset. 75% of the data are considered for training and the other 25% are considered for testing. Segmentation of the classified results is performed. Simulation resultsprove that the DL model from scratch obtains the highest performance with the augmented data. In addition, a new practical implementation is presented for the proposed models.

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.