The Vehant Technology Pvt. Ltd., Noida, India.
The Department of EE, Indian Institute of Technology Delhi, Delhi 110016, India.
Comput Biol Med. 2022 May;144:105350. doi: 10.1016/j.compbiomed.2022.105350. Epub 2022 Mar 3.
Corona Virus Disease-2019 (COVID-19), caused by Severe Acute Respiratory Syndrome-Corona Virus-2 (SARS-CoV-2), is a highly contagious disease that has affected the lives of millions around the world. Chest X-Ray (CXR) and Computed Tomography (CT) imaging modalities are widely used to obtain a fast and accurate diagnosis of COVID-19. However, manual identification of the infection through radio images is extremely challenging because it is time-consuming and highly prone to human errors. Artificial Intelligence (AI)-techniques have shown potential and are being exploited further in the development of automated and accurate solutions for COVID-19 detection. Among AI methodologies, Deep Learning (DL) algorithms, particularly Convolutional Neural Networks (CNN), have gained significant popularity for the classification of COVID-19. This paper summarizes and reviews a number of significant research publications on the DL-based classification of COVID-19 through CXR and CT images. We also present an outline of the current state-of-the-art advances and a critical discussion of open challenges. We conclude our study by enumerating some future directions of research in COVID-19 imaging classification.
新型冠状病毒病-2019（COVID-19），由严重急性呼吸系统综合症-冠状病毒-2（SARS-CoV-2）引起，是一种高传染性疾病，已影响全球数百万人的生活。胸部 X 射线（CXR）和计算机断层扫描（CT）成像方式被广泛用于快速准确地诊断 COVID-19。然而，通过无线电图像手动识别感染极具挑战性，因为它耗时且极易出错。人工智能（AI）技术已显示出潜力，并在开发 COVID-19 检测的自动化和准确解决方案方面得到了进一步的利用。在 AI 方法中，深度学习（DL）算法，特别是卷积神经网络（CNN），已在 CXR 和 CT 图像的 COVID-19 分类方面得到了广泛的关注。本文通过 CXR 和 CT 图像总结和回顾了大量关于基于 DL 的 COVID-19 分类的重要研究出版物。我们还概述了当前的最新进展，并对开放挑战进行了批判性讨论。最后，我们列举了 COVID-19 成像分类的一些未来研究方向。
