Deep Transfer Learning Based Detection of COVID-19 from Chest X-ray Images

Abstract

The novel COVID-19 coronavirus has become an important threatening issue now for billions of humans. But lacking of available testing kits lead us to develop automatic detection system to prevent the spread of COVID-19. Since, most COVID-19 patients suffer the lung infection, chest X-ray can be used to be an effective imaging technique to detect COVID-19. Because of the limited number of COVID-19 chest X-ray images, it is efficient to use deep transfer learning that can provide a promising solution by transferring knowledge from generic object recognition task to domain-specific task. In this work, we aim to develop a deep transfer learning based detection of COVID-19 from chest X-ray images using imageNet pre-trained VGG-16 deep CNN model. We develop six binary classifiers for different chest X-ray image data and then integrate all the classifiers to produce a final classifier to predict COVID-19 from non COVID-19 cases (normal and other disease). To evaluate all the models, we have collected COVID-19 chest X-ray images from two open source GitHub repository and others chest X-ray images from RSNA Pneumonia Detection Challenge dataset. The experimental result shows the capability of our final classifier in the detection of COVID-19 by evaluating it with an independent testset. It also shows that our classifier achieved a high accuracy of 93% (with sensitivity of 87%, specificity of 94%, precision of 100% and F1-Score of 93%) in the detection of COVID-19 X-ray images from normal and other disease.