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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...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2021
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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. |
format | Online Article Text |
id | pubmed-7904146 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
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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