USE OF DEEP LEARNING TO DIAGNOSE COVID-19 BASED ON COMPUTED TOMOGRAPHY IMAGES

Abstract

The newly identified Coronavirus pneumonia, later called COVID-19, is highly transmissible and pathogenic. The most common symptoms of this disease are dry cough, sore throat, and fever. Symptoms can progress to a severe form of pneumonia with critical complications, including septic shock, pulmonary edema, acute respiratory distress syndrome, and multiple organ failure. A major obstacle in controlling the spread of this disease is the inefficiency and scarcity of medical tests. Increasing efforts have been made to develop deep learning (DL) methods to diagnose COVID-19 based on tomography images. These computer-aided diagnostic systems can assist in the early detection of abnormalities in COVID-19 and facilitate the monitoring of disease progression, potentially reducing mortality rates. In this study, we compared the popular resource extraction structures based on deep learning for the automatic classification of COVID-19. To obtain a more precise method, which is an essential learning component, a set of deep convolutional neural networks (CNN) was chosen to train our model. The performance of the proposed method was validated using a COVID-19 dataset with computed tomography (CT) images. This dataset is available to the public and contains hundreds of positive CT scans for the disease. DL methods were performed and the best classified CNN was able to achieve excellent diagnostic results for COVID-19.