Diagnose of COVID-19 by Using CNN Based Models on Medical Images

Abstract

COVID-19 is a fast-spreading viral disease that affects both animals and people. Chest computed tomography and chest radiography are superior imaging techniques for detecting lung problems. This work was done to diagnose the COVID-19 disease by using CNN-based models on chest gray scale CT scan images. In the current study, the poster anterior view of the chest CT scan images has been used for both healthy subjects and patients with COVID-19 illness. Using the deep learning on CNN based models; we compared the performance of cleaned as well as augmented models. We have contrasted and compared precision of the Inception V3, Xception, and ResNeXt models. The dataset was generated from the Kaggle repository; there were 15102 gray scale chest CT scan pictures in the collected data set, including normal, COVID, and pneumonia. Total data set is further divided into training and validation sets.  The Xception model detects images of chest CT scan with an accuracy of 98.90%, which is higher than state of the art approaches. This study makes medical claims and examines different classification schemes for patients infected with COVID-19.