Kumar Aayush, Tripathi Ayush R, Satapathy Suresh Chandra, Zhang Yu-Dong
School of Computer Engineering, Kalinga Institute of Industrial Technology (Deemed to Be University), Bhubaneswar, Odisha, 751024, India.
Department of Informatics, University of Leicester, Leicester LE1 7RH, UK.
Pattern Recognit. 2022 Feb;122:108255. doi: 10.1016/j.patcog.2021.108255. Epub 2021 Aug 25.
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.
新冠疫情已成为人类有史以来遭遇的最致命大流行病之一。筛查检测是目前检测患者体内严重急性呼吸综合征冠状病毒最可靠、最准确的步骤，最常用的是逆转录聚合酶链反应（RT-PCR）检测。众多研究人员和早期研究表明，患者胸部X光（CXR）或计算机断层扫描（CT）影像中的视觉指标（异常情况）是新冠患者的一个重要特征，可用于在大量人群中发现病毒。受开源社区为应对新冠疫情所做各种贡献的激励，我们推出了SARS-Net，这是一种计算机辅助诊断（CADx）系统，它结合了图卷积网络和卷积神经网络，用于检测患者CXR影像中的异常情况，以判断患者是否感染新冠病毒。在本文中，我们介绍并评估了一种定制的深度学习架构SARS-Net的性能，用于对胸部X光图像进行分类和检测，以诊断新冠病毒。定量分析表明，所提出的模型比之前提到的最先进方法具有更高的准确率。结果发现，我们提出的模型在验证集上的准确率达到了97.60%以及灵敏度达到了92.90%。
