Implementation of Xception and EfficientNetB3 for COVID-19 Detection on Chest X-Ray Image via Transfer Learning

Abstract

COVID-19 is a highly contagious infectious disease caused by the SARS-CoV-2 virus that can cause respiratory issues. The utilization of X-ray imaging has the potential to serve as an alternative means of detecting COVID-19 by offering insights into the condition of the lungs. Rapid and automated analysis of medical images and patterns can be achieved through deep learning techniques. In this study, we propose methods for the automatic classification of COVID-19 from Chest X-Ray images using CNN with transfer learning techniques, namely Xception and EfficientNetB3 architectures, as well as an ensemble of both architectures working in parallel. Additionally, we use Grad-CAM to visualize the regions of the X-ray image that are most important for the classification decision. The classification of COVID-19 is carried out for four types of classes: COVID-19, normal, bacterial pneumonia, and viral pneumonia. The proposed classifier models achieve an overall accuracy of 94.44% for the Xception classifier, 95.28% for the EfficientNetB3 classifier, and 94.44% for the parallel classifier. The accuracy value is higher than the other comparison classifiers accuracy values. Overall, the proposed classifiers can be recommended as tools to assist radiologists and clinical practitioners in diagnosing and following up with COVID-19 cases.