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A systematic literature review of machine learning application in COVID-19 medical image classification

Detecting COVID-19 as early as possible and quickly is one way to stop the spread of COVID-19. Machine learning development can help to diagnose COVID-19 more quickly and accurately. This report aims to find out how far research has progressed and what lessons can be learned for future research in t...

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Detalles Bibliográficos
Autores principales: Daniel, Cenggoro, Tjeng Wawan, Pardamean, Bens
Formato: Online Artículo Texto
Lenguaje:English
Publicado: The Author(s). Published by Elsevier B.V. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9829419/
https://www.ncbi.nlm.nih.gov/pubmed/36643182
http://dx.doi.org/10.1016/j.procs.2022.12.192
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author Daniel
Cenggoro, Tjeng Wawan
Pardamean, Bens
author_facet Daniel
Cenggoro, Tjeng Wawan
Pardamean, Bens
author_sort Daniel
collection PubMed
description Detecting COVID-19 as early as possible and quickly is one way to stop the spread of COVID-19. Machine learning development can help to diagnose COVID-19 more quickly and accurately. This report aims to find out how far research has progressed and what lessons can be learned for future research in this sector. By filtering titles, abstracts, and content in the Google Scholar database, this literature review was able to find 19 related papers to answer two research questions, i.e. what medical images are commonly used for COVID-19 classification and what are the methods for COVID-19 classification. According to the findings, chest X-ray were the most commonly used data to categorize COVID-19 and transfer learning techniques were the method used in this study. Researchers also concluded that lung segmentation and use of multimodal data could improve performance.
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spelling pubmed-98294192023-01-10 A systematic literature review of machine learning application in COVID-19 medical image classification Daniel Cenggoro, Tjeng Wawan Pardamean, Bens Procedia Comput Sci Article Detecting COVID-19 as early as possible and quickly is one way to stop the spread of COVID-19. Machine learning development can help to diagnose COVID-19 more quickly and accurately. This report aims to find out how far research has progressed and what lessons can be learned for future research in this sector. By filtering titles, abstracts, and content in the Google Scholar database, this literature review was able to find 19 related papers to answer two research questions, i.e. what medical images are commonly used for COVID-19 classification and what are the methods for COVID-19 classification. According to the findings, chest X-ray were the most commonly used data to categorize COVID-19 and transfer learning techniques were the method used in this study. Researchers also concluded that lung segmentation and use of multimodal data could improve performance. The Author(s). Published by Elsevier B.V. 2023 2023-01-10 /pmc/articles/PMC9829419/ /pubmed/36643182 http://dx.doi.org/10.1016/j.procs.2022.12.192 Text en © 2022 The Author(s). Published by Elsevier B.V. Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active.
spellingShingle Article
Daniel
Cenggoro, Tjeng Wawan
Pardamean, Bens
A systematic literature review of machine learning application in COVID-19 medical image classification
title A systematic literature review of machine learning application in COVID-19 medical image classification
title_full A systematic literature review of machine learning application in COVID-19 medical image classification
title_fullStr A systematic literature review of machine learning application in COVID-19 medical image classification
title_full_unstemmed A systematic literature review of machine learning application in COVID-19 medical image classification
title_short A systematic literature review of machine learning application in COVID-19 medical image classification
title_sort systematic literature review of machine learning application in covid-19 medical image classification
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9829419/
https://www.ncbi.nlm.nih.gov/pubmed/36643182
http://dx.doi.org/10.1016/j.procs.2022.12.192
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