Diagnosis of COVID-19 cases from viral pneumonia and normal ones based on transfer learning approach: Xception-GRU

Abstract

The World Health Organization (WHO) considered it difficult to describe the information about the spread of critical symptoms of the Coronavirus due to the different behaviors of the COVID −19 virus. Most people only experience symptoms when the symptoms of the Coronavirus reach an acute stage, and others do not experience any symptoms at all. Lung scan images are one of the ways to distinguish COVID-19 from other similar diseases, such as pneumonia. The emerging novel of the coronavirus and the similarity of pulmonary complications cause the doctor to misdiagnose. In this paper, we utilize 13967 samples of lung scan images to diagnose COVID-19 cases from viral pneumonia and normal ones. This paper proposes an Xception based transfer learning approach to extract the deep features of each image based on depthwise separable convolutions. We extend the Xception architecture by adding a Gated Recurrent Unit (GRU) and a fully connected layer and fine-tune the model to adjust a more abstract representation of features to classify them. The obtained results show the effectiveness of our proposed hybrid method in detecting cases of COVID-19 from normal and viral pneumonia with an accuracy and precision of 95.71% and 94.24%, respectively, which improves the state-of-the-art results.