Bhattacharya Sweta, Reddy Maddikunta Praveen Kumar, Pham Quoc-Viet, Gadekallu Thippa Reddy, Krishnan S Siva Rama, Chowdhary Chiranji Lal, Alazab Mamoun, Jalil Piran Md
School of Information Technology and Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, India.
Research Institute of Computer, Information and Communication, Pusan National University, Busan 46241, Republic of Korea.
Sustain Cities Soc. 2021 Feb;65:102589. doi: 10.1016/j.scs.2020.102589. Epub 2020 Nov 5.
Since December 2019, the coronavirus disease (COVID-19) outbreak has caused many death cases and affected all sectors of human life. With gradual progression of time, COVID-19 was declared by the world health organization (WHO) as an outbreak, which has imposed a heavy burden on almost all countries, especially ones with weaker health systems and ones with slow responses. In the field of healthcare, deep learning has been implemented in many applications, e.g., diabetic retinopathy detection, lung nodule classification, fetal localization, and thyroid diagnosis. Numerous sources of medical images (e.g., X-ray, CT, and MRI) make deep learning a great technique to combat the COVID-19 outbreak. Motivated by this fact, a large number of research works have been proposed and developed for the initial months of 2020. In this paper, we first focus on summarizing the state-of-the-art research works related to deep learning applications for COVID-19 medical image processing. Then, we provide an overview of deep learning and its applications to healthcare found in the last decade. Next, three use cases in China, Korea, and Canada are also presented to show deep learning applications for COVID-19 medical image processing. Finally, we discuss several challenges and issues related to deep learning implementations for COVID-19 medical image processing, which are expected to drive further studies in controlling the outbreak and controlling the crisis, which results in smart healthy cities.
自2019年12月以来，冠状病毒病（COVID-19）疫情已导致许多死亡病例，并影响到人类生活的各个领域。随着时间的逐渐推移，世界卫生组织（WHO）宣布COVID-19为大流行，这给几乎所有国家都带来了沉重负担，尤其是那些卫生系统较弱和应对迟缓的国家。在医疗保健领域，深度学习已在许多应用中得到应用，例如糖尿病视网膜病变检测、肺结节分类、胎儿定位和甲状腺诊断。大量的医学图像来源（如X光、CT和MRI）使深度学习成为对抗COVID-19疫情的一项伟大技术。受这一事实的推动，在2020年的最初几个月里，人们提出并开展了大量的研究工作。在本文中，我们首先着重总结与COVID-19医学图像处理的深度学习应用相关的最新研究工作。然后，我们概述深度学习及其在过去十年中在医疗保健领域的应用。接下来，还介绍了中国、韩国和加拿大的三个用例，以展示深度学习在COVID-19医学图像处理中的应用。最后，我们讨论了与COVID-19医学图像处理的深度学习实现相关的几个挑战和问题，这些挑战和问题有望推动在控制疫情和控制危机方面的进一步研究，从而打造智能健康城市。
