Detection of COVID-19 Using Chest Radiographs with Intelligent Deployment Architecture

Abstract

The outbreak of Coronavirus Disease (COVID-19) has caused a huge disturbance globally. The problem is the unavailability of vaccines and limited resources for its detection. In this paper, authors have carried out a case study of India to analyse the problem faced by the authorities for detecting COVID-19 amongst the suspected cases and have tried to solve the problem using a Deep Neural Network-based approach for analyzing chest x-rays in order to detect the onset/presence of related disease. After obtaining data from available resources, we trained a transfer learning-based CNN model. The model tries to extract the features of the radiographs and thus classifies it into the appropriate class. Heat map filter was used on the images significantly helping the model to perform better. This paper presents the validation of the model on certain test images and shows that the model is reliable to an extent. This paper also demonstrates a general architecture for the deployment of the model as per the considered case study.