Deep Learning Models for Classification and Localization of COVID-19 Abnormalities on Chest Radiographs

Abstract

COVID-19 pandemic is five times more deadly than flu and other disease. It causes serious morbidity and mortality across the world. Like other pneumonias, pulmonary infection with COVID-19 results in fluids in the lungs and inflammation. Equally, the disease looks very similar to other bacterial and viral pneumonias on chest radiographs; as such it is very difficult to be diagnosed. In this work, Convolutional Neural Network (CNN), Faster Region Based Convolutional Neural Network (Faster R-CNN) and Chest X-ray Network (CheXNet) deep learning algorithms were used to develop models for classification and localization of COVID-19 abnormalities on chest radiographs models for normal and opacity (typical, atypical, indeterminate) cases in order to help medical doctors, radiologists and other health workers to provide fast and confident diagnosis of the COVID-19. Hence, CheXNet based model has comparatively outperformed other models for being able to classify chest radiographs as negative for pneumonia or typical, indeterminate and atypical for COVID-19 pandemic with 97% accuracy and more so for its ability to correctly classify chest radiographs for typical, indeterminate and atypical COVID-19 pandemic cases the model has comparatively outperformed other models with 93% precision. However, for the ability to correctly classify the chest radiographs as negative for pneumonia, Faster R-CNN based model outperformed other models with 94% recall.