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Deep Learning Models for the Diagnosis and Screening of COVID-19: A Systematic Review
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...
Autores principales: | , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Springer Nature Singapore
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9312319/ https://www.ncbi.nlm.nih.gov/pubmed/35911439 http://dx.doi.org/10.1007/s42979-022-01326-3 |
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author | Siddiqui, Shah Arifeen, Murshedul Hopgood, Adrian Good, Alice Gegov, Alexander Hossain, Elias Rahman, Wahidur Hossain, Shazzad Al Jannat, Sabila Ferdous, Rezowan Masum, Shamsul |
author_facet | Siddiqui, Shah Arifeen, Murshedul Hopgood, Adrian Good, Alice Gegov, Alexander Hossain, Elias Rahman, Wahidur Hossain, Shazzad Al Jannat, Sabila Ferdous, Rezowan Masum, Shamsul |
author_sort | Siddiqui, Shah |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-9312319 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Springer Nature Singapore |
record_format | MEDLINE/PubMed |
spelling | pubmed-93123192022-07-26 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 SN Comput Sci Review Article 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. Springer Nature Singapore 2022-07-25 2022 /pmc/articles/PMC9312319/ /pubmed/35911439 http://dx.doi.org/10.1007/s42979-022-01326-3 Text en © Crown 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Review Article Siddiqui, Shah Arifeen, Murshedul Hopgood, Adrian Good, Alice Gegov, Alexander Hossain, Elias Rahman, Wahidur Hossain, Shazzad Al Jannat, Sabila Ferdous, Rezowan Masum, Shamsul Deep Learning Models for the Diagnosis and Screening of COVID-19: A Systematic Review |
title | Deep Learning Models for the Diagnosis and Screening of COVID-19: A Systematic Review |
title_full | Deep Learning Models for the Diagnosis and Screening of COVID-19: A Systematic Review |
title_fullStr | Deep Learning Models for the Diagnosis and Screening of COVID-19: A Systematic Review |
title_full_unstemmed | Deep Learning Models for the Diagnosis and Screening of COVID-19: A Systematic Review |
title_short | Deep Learning Models for the Diagnosis and Screening of COVID-19: A Systematic Review |
title_sort | deep learning models for the diagnosis and screening of covid-19: a systematic review |
topic | Review Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9312319/ https://www.ncbi.nlm.nih.gov/pubmed/35911439 http://dx.doi.org/10.1007/s42979-022-01326-3 |
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