A precise and timely graph‐based approach to identify SARS Covid19 infection from medical imaging data using IsoCovNet

Abstract

More than 100 million individuals have been infected by the COVID19 virus since 2019. Even if the vaccination procedure has already begun, it will take time to attain an adequate supply. There have been several efforts by computer scientists to filter COVID19 from CXR or CT scans due to the disease's extensive prevalence. These patients' CT and CXR scans are utilized to identify COVID19 using IsoCovNet, a Graph-Isomorphic-Network, that is, GIN-based model for detecting COVID19. A GIN-based design dictates that our suggested model only takes data in the form of graphs. At the outset, the input image undergoes a conversion into an unordered network, that is, a graph that considers only the links between elements rather than the entire image. This approach significantly reduces the model's processing time. We verified the effectiveness of our proposed IsoCovNet network by using four datasets, which consist of both x-ray and CT-scan images, from five standard sources that are publicly available on platforms like Kaggle and GitHub. The network achieved an accuracy of 99.51% on binary datasets and a higher accuracy of 99.84% on the multi-classification task of detecting Covid19.