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

被引:76
|
作者
Subramanian, Nandhini [1 ]
Elharrouss, Omar [1 ]
Al-Maadeed, Somaya [1 ]
Chowdhury, Muhammed [1 ]
机构
[1] Qatar Univ, Coll Engn Comp Sci & Engn, Doha, Qatar
关键词
COVID-19; detection; DL-Based COVID-19 detection; Lung image classification; Coronavirus pandemic; Medical image processing;
D O I
10.1016/j.compbiomed.2022.105233
中图分类号
Q [生物科学];
学科分类号
07 ; 0710 ; 09 ;
摘要
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.
引用
收藏
页数:12
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