Deep convolutional neural networks for COVID‐19 automatic diagnosis

Emara, Heba M.; Shoaib, Mohamed R.; Elwekeil, Mohamed; El‐Shafai, Walid; Taha, Taha E.; El‐Fishawy, Adel S.; El‐Rabaie, El‐Sayed M.; Alshebeili, Saleh A.; Dessouky, Moawad I.; Abd El‐Samie, Fathi E.

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.