COVID-19 Along with Other Chest Infection Diagnoses Using Faster R-CNN and Generative Adversarial Network

Abstract

The rapid spreading of coronavirus (COVID-19) caused severe respiratory infections affecting the lungs. Automatic diagnosis helps to fight against COVID-19 in community outbreaks. Medical imaging technology can reinforce disease monitoring and detection facilities with the advancement of computer vision. Unfortunately, deep learning models are facing starvation of more generalized datasets as the data repositories of COVID-19 are not rich enough to provide significant distinct features. To address the limitation, this article describes the generation of synthetic images of COVID-19 along with other chest infections with distinct features by empirical top entropy-based patch selection approach using the generative adversarial network. After that, a diagnosis is performed through a faster region-based convolutional neural network using 6,406 synthetic as well as 3,933 original chest X-ray images of different chest infections, which also addressed the data imbalance problems and not recumbent to a particular class. The experiment confirms a satisfactory COVID-19 diagnosis accuracy of 99.16% in a multi-class scenario.