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Deep Convolutional Approaches for the Analysis of COVID-19 Using Chest X-Ray Images From Portable Devices
The recent human coronavirus disease (COVID-19) is a respiratory infection caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Given the effects of COVID-19 in pulmonary tissues, chest radiography imaging plays an important role in the screening, early detection, and monitoring o...
Formato: | Online Artículo Texto |
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Lenguaje: | English |
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IEEE
2020
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8545263/ https://www.ncbi.nlm.nih.gov/pubmed/34786295 http://dx.doi.org/10.1109/ACCESS.2020.3033762 |
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collection | PubMed |
description | The recent human coronavirus disease (COVID-19) is a respiratory infection caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Given the effects of COVID-19 in pulmonary tissues, chest radiography imaging plays an important role in the screening, early detection, and monitoring of the suspected individuals. Hence, as the pandemic of COVID-19 progresses, there will be a greater reliance on the use of portable equipment for the acquisition of chest X-ray images due to its accessibility, widespread availability, and benefits regarding to infection control issues, minimizing the risk of cross-contamination. This work presents novel fully automatic approaches specifically tailored for the classification of chest X-ray images acquired by portable equipment into 3 different clinical categories: normal, pathological, and COVID-19. For this purpose, 3 complementary deep learning approaches based on a densely convolutional network architecture are herein presented. The joint response of all the approaches allows to enhance the differentiation between patients infected with COVID-19, patients with other diseases that manifest characteristics similar to COVID-19 and normal cases. The proposed approaches were validated over a dataset specifically retrieved for this research. Despite the poor quality of the chest X-ray images that is inherent to the nature of the portable equipment, the proposed approaches provided global accuracy values of 79.62%, 90.27% and 79.86%, respectively, allowing a reliable analysis of portable radiographs to facilitate the clinical decision-making process. |
format | Online Article Text |
id | pubmed-8545263 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | IEEE |
record_format | MEDLINE/PubMed |
spelling | pubmed-85452632021-11-12 Deep Convolutional Approaches for the Analysis of COVID-19 Using Chest X-Ray Images From Portable Devices IEEE Access Computational and Artificial Intelligence The recent human coronavirus disease (COVID-19) is a respiratory infection caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Given the effects of COVID-19 in pulmonary tissues, chest radiography imaging plays an important role in the screening, early detection, and monitoring of the suspected individuals. Hence, as the pandemic of COVID-19 progresses, there will be a greater reliance on the use of portable equipment for the acquisition of chest X-ray images due to its accessibility, widespread availability, and benefits regarding to infection control issues, minimizing the risk of cross-contamination. This work presents novel fully automatic approaches specifically tailored for the classification of chest X-ray images acquired by portable equipment into 3 different clinical categories: normal, pathological, and COVID-19. For this purpose, 3 complementary deep learning approaches based on a densely convolutional network architecture are herein presented. The joint response of all the approaches allows to enhance the differentiation between patients infected with COVID-19, patients with other diseases that manifest characteristics similar to COVID-19 and normal cases. The proposed approaches were validated over a dataset specifically retrieved for this research. Despite the poor quality of the chest X-ray images that is inherent to the nature of the portable equipment, the proposed approaches provided global accuracy values of 79.62%, 90.27% and 79.86%, respectively, allowing a reliable analysis of portable radiographs to facilitate the clinical decision-making process. IEEE 2020-10-26 /pmc/articles/PMC8545263/ /pubmed/34786295 http://dx.doi.org/10.1109/ACCESS.2020.3033762 Text en This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/ https://creativecommons.org/licenses/by/4.0/This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/ |
spellingShingle | Computational and Artificial Intelligence Deep Convolutional Approaches for the Analysis of COVID-19 Using Chest X-Ray Images From Portable Devices |
title | Deep Convolutional Approaches for the Analysis of COVID-19 Using Chest X-Ray Images From Portable Devices |
title_full | Deep Convolutional Approaches for the Analysis of COVID-19 Using Chest X-Ray Images From Portable Devices |
title_fullStr | Deep Convolutional Approaches for the Analysis of COVID-19 Using Chest X-Ray Images From Portable Devices |
title_full_unstemmed | Deep Convolutional Approaches for the Analysis of COVID-19 Using Chest X-Ray Images From Portable Devices |
title_short | Deep Convolutional Approaches for the Analysis of COVID-19 Using Chest X-Ray Images From Portable Devices |
title_sort | deep convolutional approaches for the analysis of covid-19 using chest x-ray images from portable devices |
topic | Computational and Artificial Intelligence |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8545263/ https://www.ncbi.nlm.nih.gov/pubmed/34786295 http://dx.doi.org/10.1109/ACCESS.2020.3033762 |
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