A review of deep learning-based detection methods for COVID-19
Author | Subramanian, N. |
Author | Elharrouss, O. |
Author | Al-Maadeed, Somaya |
Author | Chowdhury, M. |
Available date | 2022-05-19T10:23:05Z |
Publication Date | 2022 |
Publication Name | Computers in Biology and Medicine |
Resource | Scopus |
Identifier | http://dx.doi.org/10.1016/j.compbiomed.2022.105233 |
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. |
Language | en |
Publisher | Elsevier Ltd |
Subject | Biological organs Computerized tomography Deep learning Image classification Medical imaging Coronavirus pandemic Coronaviruses COVID-19 detection Detection methods DL-based COVID-19 detection Evaluation metrics Images classification Learning methods Lung image classification Medical images processing Coronavirus |
Type | Article |
Volume Number | 143 |
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Computer Science & Engineering [2286 items ]
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COVID-19 Research [809 items ]