SARS-Net: COVID-19 detection from chest x-rays by combining graph convolutional network and convolutional neural network

COVID-19 has emerged as one of the deadliest pandemics that has ever crept on humanity. Screening tests are currently the most reliable and accurate steps in detecting severe acute respiratory syndrome coronavirus in a patient, and the most used is RT-PCR testing. Various researchers and early studies im-plied that visual indicators (abnormalities) in a patient's Chest X-Ray (CXR) or computed tomography (CT) imaging were a valuable characteristic of a COVID-19 patient that can be leveraged to find out virus in a vast population. Motivated by various contributions to open-source community to tackle COVID-19 pandemic, we introduce SARS-Net, a CADx system combining Graph Convolutional Networks and Convo-lutional Neural Networks for detecting abnormalities in a patient's CXR images for presence of COVID-19 infection in a patient. In this paper, we introduce and evaluate the performance of a custom-made deep learning architec-ture SARS-Net, to classify and detect the Chest X-ray images for COVID-19 diagnosis. Quantitative analysis shows that the proposed model achieves more accuracy than previously mentioned state-of-the-art meth-ods. It was found that our proposed model achieved an accuracy of 97.60% and a sensitivity of 92.90% on the validation set. (c) 2021 Elsevier Ltd. All rights reserved.

Automated summary

Screening tests such as RT-PCR are crucial in detecting SARS-CoV-2, with visual indicators in Chest X-Ray images being valuable characteristics that can help identify the virus. The use of SARS-Net, a CADx system combining Graph Convolutional Networks and Convolutional Neural Networks, has shown promising results in classifying and detecting abnormalities in CXR images for COVID-19 diagnosis, achieving high accuracy and sensitivity rates.