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基于深度学习的新型冠状病毒肺炎检测方法综述

A review of deep learning-based detection methods for COVID-19.

作者信息

Subramanian Nandhini, Elharrouss Omar, Al-Maadeed Somaya, Chowdhury Muhammed

机构信息

Qatar University College of Engineering, Computer Science and Engineering, Qatar.

出版信息

Comput Biol Med. 2022 Apr;143:105233. doi: 10.1016/j.compbiomed.2022.105233. Epub 2022 Jan 29.

Abstract

COVID-19 is a fast-spreading pandemic, and early detection is crucial for stopping the spread of infection. Lung images are used in the detection of coronavirus infection. Chest X-ray (CXR) and computed tomography (CT) images are available for the detection of COVID-19. Deep learning methods have been proven efficient and better performing in many computer vision and medical imaging applications. In the rise of the COVID pandemic, researchers are using deep learning methods to detect coronavirus infection in lung images. In this paper, the currently available deep learning methods that are used to detect coronavirus infection in lung images are surveyed. The available methodologies, public datasets, datasets that are used by each method and evaluation metrics are summarized in this paper to help future researchers. The evaluation metrics that are used by the methods are comprehensively compared.

摘要

新冠病毒病(COVID-19)是一种快速传播的大流行病,早期检测对于阻止感染传播至关重要。肺部图像用于冠状病毒感染的检测。胸部X光(CXR)和计算机断层扫描(CT)图像可用于COVID-19的检测。深度学习方法在许多计算机视觉和医学成像应用中已被证明是高效且性能更佳的。在新冠大流行期间,研究人员正在使用深度学习方法来检测肺部图像中的冠状病毒感染。本文对目前用于检测肺部图像中冠状病毒感染的深度学习方法进行了综述。本文总结了可用的方法、公共数据集、每种方法使用的数据集以及评估指标,以帮助未来的研究人员。对这些方法所使用的评估指标进行了全面比较。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0ef9/8798789/b7f7c67edba6/ga1_lrg.jpg

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