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A comprehensive review of analyzing the chest X-ray images to detect COVID-19 infections using deep learning techniques
COVID-19, a highly infectious respiratory disease a used by SARS virus, has killed millions of people across many countries. To enhance quick and accurate diagnosis of COVID-19, chest X-ray (CXR) imaging methods were commonly utilized. Identifying the infection manually by radio imaging, on the othe...
Autores principales: | , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer Berlin Heidelberg
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10220331/ https://www.ncbi.nlm.nih.gov/pubmed/37362273 http://dx.doi.org/10.1007/s00500-023-08561-7 |
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author | Subramaniam, Kavitha Palanisamy, Natesan Sinnaswamy, Renugadevi Ammapalayam Muthusamy, Suresh Mishra, Om Prava Loganathan, Ashok Kumar Ramamoorthi, Ponarun Gnanakkan, Christober Asir Rajan Charles Thangavel, Gunasekaran Sundararajan, Suma Christal Mary |
author_facet | Subramaniam, Kavitha Palanisamy, Natesan Sinnaswamy, Renugadevi Ammapalayam Muthusamy, Suresh Mishra, Om Prava Loganathan, Ashok Kumar Ramamoorthi, Ponarun Gnanakkan, Christober Asir Rajan Charles Thangavel, Gunasekaran Sundararajan, Suma Christal Mary |
author_sort | Subramaniam, Kavitha |
collection | PubMed |
description | COVID-19, a highly infectious respiratory disease a used by SARS virus, has killed millions of people across many countries. To enhance quick and accurate diagnosis of COVID-19, chest X-ray (CXR) imaging methods were commonly utilized. Identifying the infection manually by radio imaging, on the other hand, was considered, extremely difficult due to the time commitment and significant risk of human error. Emerging artificial intelligence (AI) techniques promised exploration in the development of precise and as well as automated COVID-19 detection tools. Convolution neural networks (CNN), a well performing deep learning strategy tends to gain substantial favors among AI approaches for COVID-19 classification. The preprints and published studies to diagnose COVID-19 with CXR pictures using CNN and other deep learning methodologies are reviewed and critically assessed in this research. This study focused on the methodology, algorithms, and preprocessing techniques used in various deep learning architectures, as well as datasets and performance studies of several deep learning architectures used in prediction and diagnosis. Our research concludes with a list of future research directions in COVID-19 imaging categorization. |
format | Online Article Text |
id | pubmed-10220331 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Springer Berlin Heidelberg |
record_format | MEDLINE/PubMed |
spelling | pubmed-102203312023-05-30 A comprehensive review of analyzing the chest X-ray images to detect COVID-19 infections using deep learning techniques Subramaniam, Kavitha Palanisamy, Natesan Sinnaswamy, Renugadevi Ammapalayam Muthusamy, Suresh Mishra, Om Prava Loganathan, Ashok Kumar Ramamoorthi, Ponarun Gnanakkan, Christober Asir Rajan Charles Thangavel, Gunasekaran Sundararajan, Suma Christal Mary Soft comput Application of Soft Computing COVID-19, a highly infectious respiratory disease a used by SARS virus, has killed millions of people across many countries. To enhance quick and accurate diagnosis of COVID-19, chest X-ray (CXR) imaging methods were commonly utilized. Identifying the infection manually by radio imaging, on the other hand, was considered, extremely difficult due to the time commitment and significant risk of human error. Emerging artificial intelligence (AI) techniques promised exploration in the development of precise and as well as automated COVID-19 detection tools. Convolution neural networks (CNN), a well performing deep learning strategy tends to gain substantial favors among AI approaches for COVID-19 classification. The preprints and published studies to diagnose COVID-19 with CXR pictures using CNN and other deep learning methodologies are reviewed and critically assessed in this research. This study focused on the methodology, algorithms, and preprocessing techniques used in various deep learning architectures, as well as datasets and performance studies of several deep learning architectures used in prediction and diagnosis. Our research concludes with a list of future research directions in COVID-19 imaging categorization. Springer Berlin Heidelberg 2023-05-27 /pmc/articles/PMC10220331/ /pubmed/37362273 http://dx.doi.org/10.1007/s00500-023-08561-7 Text en © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic. |
spellingShingle | Application of Soft Computing Subramaniam, Kavitha Palanisamy, Natesan Sinnaswamy, Renugadevi Ammapalayam Muthusamy, Suresh Mishra, Om Prava Loganathan, Ashok Kumar Ramamoorthi, Ponarun Gnanakkan, Christober Asir Rajan Charles Thangavel, Gunasekaran Sundararajan, Suma Christal Mary A comprehensive review of analyzing the chest X-ray images to detect COVID-19 infections using deep learning techniques |
title | A comprehensive review of analyzing the chest X-ray images to detect COVID-19 infections using deep learning techniques |
title_full | A comprehensive review of analyzing the chest X-ray images to detect COVID-19 infections using deep learning techniques |
title_fullStr | A comprehensive review of analyzing the chest X-ray images to detect COVID-19 infections using deep learning techniques |
title_full_unstemmed | A comprehensive review of analyzing the chest X-ray images to detect COVID-19 infections using deep learning techniques |
title_short | A comprehensive review of analyzing the chest X-ray images to detect COVID-19 infections using deep learning techniques |
title_sort | comprehensive review of analyzing the chest x-ray images to detect covid-19 infections using deep learning techniques |
topic | Application of Soft Computing |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10220331/ https://www.ncbi.nlm.nih.gov/pubmed/37362273 http://dx.doi.org/10.1007/s00500-023-08561-7 |
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