Transfer Learning for Automatic Detection of COVID-19 Disease in Medical Chest X-ray Images

ABSTRACT

The world has experienced the spread of a dangerous virus, Coronavirus (COVID-19), that has caused the death of millions of people worldwide at an extremely rapid rate, many studies have confirmed that the virus can be detected effectively using medical images. However, it takes a long time to analyze each image by radiologists who suffer from high pressures, especially due to the high similarity of symptoms between this virus and other respiratory diseases, which can lead to the confusion of cases and, consequently, the inability to identify them quickly, which could be a problem in a pandemic situation. In this paper, a methodology is proposed for the rapid and automatic diagnosis of this virus from chest radiographic images through the use of Artificial Intelligence (AI) techniques. There are two stages of the proposed model. The first step is data augmentation and preprocessing;the second step is the detection of COVID-19 with a transfer learning technique using a pre-trained deep convolutional network (CNN) architecture to extract features, Then, the obtained feature vectors are classified into three classes COVID-19, Normal, and pneumonia, from two open medical repositories. In the experimentation phase of our model, we evaluate a set of common metrics to measure the performance of the architecture. Experimental conclusions show an accuracy of 96.52% for all classes, then a comparison with existing models in literature demonstrates that our proposed model achieves better classification accuracy © 2022. IAENG International Journal of Computer Science.All Rights Reserved.