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AuthorSubramanian, N.
AuthorElharrouss, O.
AuthorAl-Maadeed, Somaya
AuthorChowdhury, M.
Available date2022-05-19T10:23:05Z
Publication Date2022
Publication NameComputers in Biology and Medicine
ResourceScopus
Identifierhttp://dx.doi.org/10.1016/j.compbiomed.2022.105233
URIhttp://hdl.handle.net/10576/31076
AbstractCOVID-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.
Languageen
PublisherElsevier Ltd
SubjectBiological 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
TitleA review of deep learning-based detection methods for COVID-19
TypeArticle
Volume Number143
dc.accessType Open Access


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