SARS-Net: COVID-19 detection from chest x-rays by combining graph convolutional network and convolutional neural network. (February 2022) - Kumar, Aayush; Tripathi, Ayush R; Satapathy, Suresh Chandra; Zhang, Yu-Dong
Highlights: Introduction of a new architecture SARS-Net. It is a CADx system combining graph convolutional network and convolutional neural network model for detecting abnormalities in a patient's CXR images for the presence of COVID-19 infection in a patient. We introduce and evaluate the performance of a custom-made deep learning architecture 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 the previously mentioned state-of-the-art methods. It was found that the model achieved an accuracy of 97.60% and a sensitivity of 92.90% on the validation set. Abstract: 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 implied 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 Convolutional 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 architecture 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 methods. It was found that our proposed model achieved an accuracy of 97.60% and a sensitivity of 92.90% on the validation set.