Khattab Rana, Abdelmaksoud Islam R, Abdelrazek Samir
Information Systems Department, Faculty of Computers and Information, Mansoura University, Mansoura, Egypt.
New Gener Comput. 2023;41(2):343-400. doi: 10.1007/s00354-023-00213-6. Epub 2023 Apr 4.
Coronavirus Disease 2019 (COVID-19), which is caused by Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-COV-2), surprised the world in December 2019 and has threatened the lives of millions of people. Countries all over the world closed worship places and shops, prevented gatherings, and implemented curfews to stand against the spread of COVID-19. Deep Learning (DL) and Artificial Intelligence (AI) can have a great role in detecting and fighting this disease. Deep learning can be used to detect COVID-19 symptoms and signs from different imaging modalities, such as X-Ray, Computed Tomography (CT), and Ultrasound Images (US). This could help in identifying COVID-19 cases as a first step to curing them. In this paper, we reviewed the research studies conducted from January 2020 to September 2022 about deep learning models that were used in COVID-19 detection. This paper clarified the three most common imaging modalities (X-Ray, CT, and US) in addition to the DL approaches that are used in this detection and compared these approaches. This paper also provided the future directions of this field to fight COVID-19 disease.
2019冠状病毒病（COVID-19）由严重急性呼吸综合征冠状病毒2（SARS-COV-2）引起，于2019年12月震惊全球，已威胁到数百万人的生命。世界各国关闭了宗教场所和商店，禁止集会，并实施宵禁以抗击COVID-19的传播。深度学习（DL）和人工智能（AI）在检测和对抗这种疾病方面可以发挥重要作用。深度学习可用于从不同的成像模态（如X射线、计算机断层扫描（CT）和超声图像（US））中检测COVID-19的症状和体征。这有助于将COVID-19病例识别为治愈它们的第一步。在本文中，我们回顾了2020年1月至2022年9月期间进行的关于用于COVID-19检测的深度学习模型的研究。本文除了阐明了用于此检测的深度学习方法外，还明确了三种最常见的成像模态（X射线、CT和US），并对这些方法进行了比较。本文还提供了该领域对抗COVID-19疾病的未来方向。
