Demystify the Black-box of Deep Learning Models for COVID-19 Detection from Chest CT Radiographs

Abstract

Covid 19 continues to have a catastrpoic effect on the world, causing terrible spots to appear all over the place. Due to global epidemics and doctor and healthcare personel shortages, developing an AI-based system to detect COVID in a timely and cost-effective method has become a requirement. It is also essential to detect covid from chest X-ray and CT radiographs due to their accuracy in detecting lung infection and as well as to understand the severity. Moreover, though the number of infected people around the globe is enormous, the amount of covid data set to build an AI system is scarce and scattered. In this letter, we presented a Chest CT scan data (HRCT) set for Covid and healthy patients considering a varying range of severity of COVID, which we published on kaggle, that can assist other researchers to contribute to healthcare AI. We also developed three deep learning approaches for detecting covid quickly and cheaply. Our three transfer learning-based approaches, Inception v3, Resnet 50, and VGG16, achieve accuracy of 99.8%, 91.3%, and 99.3%, respectively on unseen data. We delve deeper into the black boxes of those models to demonstrate how our model comes to a certain conclusion, and we found that, despite the low accuracy of the model based on VGG16, it detects the covid spot of images well, which we believe may further assist doctors in visualizing which regions are affected.