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SOM-LWL method for identification of COVID-19 on chest X-rays

The outbreak of coronavirus disease 2019 (COVID-19) has had an immense impact on world health and daily life in many countries. Sturdy observing of the initial site of infection in patients is crucial to gain control in the struggle with COVID-19. The early automated detection of the recent coronavi...

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Autores principales: Osman, Ahmed Hamza, Aljahdali, Hani Moetque, Altarrazi, Sultan Menwer, Ahmed, Ali
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7904146/
https://www.ncbi.nlm.nih.gov/pubmed/33626053
http://dx.doi.org/10.1371/journal.pone.0247176
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author Osman, Ahmed Hamza
Aljahdali, Hani Moetque
Altarrazi, Sultan Menwer
Ahmed, Ali
author_facet Osman, Ahmed Hamza
Aljahdali, Hani Moetque
Altarrazi, Sultan Menwer
Ahmed, Ali
author_sort Osman, Ahmed Hamza
collection PubMed
description The outbreak of coronavirus disease 2019 (COVID-19) has had an immense impact on world health and daily life in many countries. Sturdy observing of the initial site of infection in patients is crucial to gain control in the struggle with COVID-19. The early automated detection of the recent coronavirus disease (COVID-19) will help to limit its dissemination worldwide. Many initial studies have focused on the identification of the genetic material of coronavirus and have a poor detection rate for long-term surgery. The first imaging procedure that played an important role in COVID-19 treatment was the chest X-ray. Radiological imaging is often used as a method that emphasizes the performance of chest X-rays. Recent findings indicate the presence of COVID-19 in patients with irregular findings on chest X-rays. There are many reports on this topic that include machine learning strategies for the identification of COVID-19 using chest X-rays. Other current studies have used non-public datasets and complex artificial intelligence (AI) systems. In our research, we suggested a new COVID-19 identification technique based on the locality-weighted learning and self-organization map (LWL-SOM) strategy for detecting and capturing COVID-19 cases. We first grouped images from chest X-ray datasets based on their similar features in different clusters using the SOM strategy in order to discriminate between the COVID-19 and non-COVID-19 cases. Then, we built our intelligent learning model based on the LWL algorithm to diagnose and detect COVID-19 cases. The proposed SOM-LWL model improved the correlation coefficient performance results between the Covid19, no-finding, and pneumonia cases; pneumonia and no-finding cases; Covid19 and pneumonia cases; and Covid19 and no-finding cases from 0.9613 to 0.9788, 0.6113 to 1 0.8783 to 0.9999, and 0.8894 to 1, respectively. The proposed LWL-SOM had better results for discriminating COVID-19 and non-COVID-19 patients than the current machine learning-based solutions using AI evaluation measures.
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spelling pubmed-79041462021-03-02 SOM-LWL method for identification of COVID-19 on chest X-rays Osman, Ahmed Hamza Aljahdali, Hani Moetque Altarrazi, Sultan Menwer Ahmed, Ali PLoS One Research Article The outbreak of coronavirus disease 2019 (COVID-19) has had an immense impact on world health and daily life in many countries. Sturdy observing of the initial site of infection in patients is crucial to gain control in the struggle with COVID-19. The early automated detection of the recent coronavirus disease (COVID-19) will help to limit its dissemination worldwide. Many initial studies have focused on the identification of the genetic material of coronavirus and have a poor detection rate for long-term surgery. The first imaging procedure that played an important role in COVID-19 treatment was the chest X-ray. Radiological imaging is often used as a method that emphasizes the performance of chest X-rays. Recent findings indicate the presence of COVID-19 in patients with irregular findings on chest X-rays. There are many reports on this topic that include machine learning strategies for the identification of COVID-19 using chest X-rays. Other current studies have used non-public datasets and complex artificial intelligence (AI) systems. In our research, we suggested a new COVID-19 identification technique based on the locality-weighted learning and self-organization map (LWL-SOM) strategy for detecting and capturing COVID-19 cases. We first grouped images from chest X-ray datasets based on their similar features in different clusters using the SOM strategy in order to discriminate between the COVID-19 and non-COVID-19 cases. Then, we built our intelligent learning model based on the LWL algorithm to diagnose and detect COVID-19 cases. The proposed SOM-LWL model improved the correlation coefficient performance results between the Covid19, no-finding, and pneumonia cases; pneumonia and no-finding cases; Covid19 and pneumonia cases; and Covid19 and no-finding cases from 0.9613 to 0.9788, 0.6113 to 1 0.8783 to 0.9999, and 0.8894 to 1, respectively. The proposed LWL-SOM had better results for discriminating COVID-19 and non-COVID-19 patients than the current machine learning-based solutions using AI evaluation measures. Public Library of Science 2021-02-24 /pmc/articles/PMC7904146/ /pubmed/33626053 http://dx.doi.org/10.1371/journal.pone.0247176 Text en © 2021 Osman et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Osman, Ahmed Hamza
Aljahdali, Hani Moetque
Altarrazi, Sultan Menwer
Ahmed, Ali
SOM-LWL method for identification of COVID-19 on chest X-rays
title SOM-LWL method for identification of COVID-19 on chest X-rays
title_full SOM-LWL method for identification of COVID-19 on chest X-rays
title_fullStr SOM-LWL method for identification of COVID-19 on chest X-rays
title_full_unstemmed SOM-LWL method for identification of COVID-19 on chest X-rays
title_short SOM-LWL method for identification of COVID-19 on chest X-rays
title_sort som-lwl method for identification of covid-19 on chest x-rays
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7904146/
https://www.ncbi.nlm.nih.gov/pubmed/33626053
http://dx.doi.org/10.1371/journal.pone.0247176
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