A Review on Deep Learning Techniques for the Diagnosis of Novel Coronavirus (COVID-19).
A Review on Deep Learning Techniques for the Diagnosis of Novel Coronavirus (COVID-19).
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DOI：
10.1109/access.2021.3058537
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发表时间：
2021
期刊：
影响因子：
--
通讯作者：
Zeng J
中科院分区：
文献类型：
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作者：
Islam MM;Karray F;Alhajj R;Zeng J
新型冠状病毒(新冠肺炎)的爆发，在全球范围内掀起了一场浩劫，已成为近百年来最急、最重的疾病之一。新冠肺炎在全球的患病率每天都在迅速上升。虽然还没有发现针对这种大流行的疫苗，但在临床医生用于新冠肺炎自动诊断的武器库中，深度学习技术被证明是一个强大的工具。本文旨在概述最近开发的基于深度学习技术的系统，该系统使用不同的医学成像模式，如计算机断层扫描(CT)和X射线。这篇综述特别讨论了使用深度学习技术为新冠肺炎诊断开发的系统，并提供了对用于训练这些网络的知名数据集的见解。它还重点介绍了该领域的研究人员开发的数据分区技术和各种性能指标。为了获得正确的洞察力，对最近的作品进行了分类。最后，我们解决了使用深度学习方法进行新冠肺炎检测所面临的挑战，以及该研究领域未来可能的趋势。本文的目的是帮助专家(医学或其他方面)和技术人员了解深度学习技术在这方面的使用方式，以及如何进一步利用这些技术来抗击新冠肺炎的爆发。
Novel coronavirus (COVID-19) outbreak, has raised a calamitous situation all over the world and has become one of the most acute and severe ailments in the past hundred years. The prevalence rate of COVID-19 is rapidly rising every day throughout the globe. Although no vaccines for this pandemic have been discovered yet, deep learning techniques proved themselves to be a powerful tool in the arsenal used by clinicians for the automatic diagnosis of COVID-19. This paper aims to overview the recently developed systems based on deep learning techniques using different medical imaging modalities like Computer Tomography (CT) and X-ray. This review specifically discusses the systems developed for COVID-19 diagnosis using deep learning techniques and provides insights on well-known data sets used to train these networks. It also highlights the data partitioning techniques and various performance measures developed by researchers in this field. A taxonomy is drawn to categorize the recent works for proper insight. Finally, we conclude by addressing the challenges associated with the use of deep learning methods for COVID-19 detection and probable future trends in this research area. The aim of this paper is to facilitate experts (medical or otherwise) and technicians in understanding the ways deep learning techniques are used in this regard and how they can be potentially further utilized to combat the outbreak of COVID-19.