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Deep learning models for the diagnosis and screening of COVID-19: a systematic review.

Siddiqui, Shah; Arifeen, Murshedul; Hopgood, Adrian; Good, Alice; Gegov, Alexander; Hossain, Elias; Rahman, Wahidur; Hossain, Shazzad; Al Jannat, Sabila; Ferdous, Rezowan; Masum, Shamsul

Authors

Shah Siddiqui

Adrian Hopgood

Alice Good

Alexander Gegov

Elias Hossain

Wahidur Rahman

Shazzad Hossain

Sabila Al Jannat

Rezowan Ferdous

Shamsul Masum



Abstract

COVID-19, caused by SARS-CoV-2, has been declared as a global pandemic by WHO. Early diagnosis of COVID-19 patients may reduce the impact of coronavirus using modern computational methods like deep learning. Various deep learning models based on CT and chest X-ray images are studied and compared in this study as an alternative solution to reverse transcription-polymerase chain reactions. This study consists of three stages: planning, conduction, and analysis/reporting. In the conduction stage, inclusion and exclusion criteria are applied to the literature searching and identification. Then, we have implemented quality assessment rules, where over 75 scored articles in the literature were included. Finally, in the analysis/reporting stage, all the papers are reviewed and analysed. After the quality assessment of the individual papers, this study adopted 57 articles for the systematic literature review. From these reviews, the critical analysis of each paper, including the represented matrix for the model evaluation, existing contributions, and motivation, has been tracked with suitable illustrations. We have also interpreted several insights of each paper with appropriate annotation. Further, a set of comparisons has been enumerated with suitable discussion. Convolutional neural networks are the most commonly used deep learning architecture for COVID-19 disease classification and identification from X-ray and CT images. Various prior studies did not include data from a hospital setting nor did they consider data preprocessing before training a deep learning model.

Citation

SIDDIQUI, S., ARIFEEN, M.A., HOPGOOD, A., GOOD, A., GEGOV, A., HOSSAIN, E., RAHMAN, W., HOSSAIN, S., AL JANNAT, S., FERDOUS, R. and MASUM, S. 2022. Deep learning models for the diagnosis and screening of COVID-19: a systematic review. SN computer science [online], 3(5), article 397. Available from: https://doi.org/10.1007/s42979-022-01326-3

Journal Article Type Article
Acceptance Date Apr 11, 2022
Online Publication Date Jul 25, 2022
Publication Date Sep 30, 2022
Deposit Date Aug 5, 2022
Publicly Available Date Aug 5, 2022
Journal SN Computer Science
Print ISSN 2661-8907
Publisher Springer
Peer Reviewed Peer Reviewed
Volume 3
Issue 5
Article Number 397
DOI https://doi.org/10.1007/s42979-022-01326-3
Keywords Coronavirus (COVID-19); RT-PCR; Machine learning (ML); Deep learning (DL); X-ray images; Computed tomography (CT) images
Public URL https://rgu-repository.worktribe.com/output/1721735

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