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Impact of Lung Segmentation on the Diagnosis and Explanation of COVID-19 in Chest X-ray Images
COVID-19 frequently provokes pneumonia, which can be diagnosed using imaging exams. Chest X-ray (CXR) is often useful because it is cheap, fast, widespread, and uses less radiation. Here, we demonstrate the impact of lung segmentation in COVID-19 identification using CXR images and evaluate which co...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8587284/ https://www.ncbi.nlm.nih.gov/pubmed/34770423 http://dx.doi.org/10.3390/s21217116 |
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author | Teixeira, Lucas O. Pereira, Rodolfo M. Bertolini, Diego Oliveira, Luiz S. Nanni, Loris Cavalcanti, George D. C. Costa, Yandre M. G. |
author_facet | Teixeira, Lucas O. Pereira, Rodolfo M. Bertolini, Diego Oliveira, Luiz S. Nanni, Loris Cavalcanti, George D. C. Costa, Yandre M. G. |
author_sort | Teixeira, Lucas O. |
collection | PubMed |
description | COVID-19 frequently provokes pneumonia, which can be diagnosed using imaging exams. Chest X-ray (CXR) is often useful because it is cheap, fast, widespread, and uses less radiation. Here, we demonstrate the impact of lung segmentation in COVID-19 identification using CXR images and evaluate which contents of the image influenced the most. Semantic segmentation was performed using a U-Net CNN architecture, and the classification using three CNN architectures (VGG, ResNet, and Inception). Explainable Artificial Intelligence techniques were employed to estimate the impact of segmentation. A three-classes database was composed: lung opacity (pneumonia), COVID-19, and normal. We assessed the impact of creating a CXR image database from different sources, and the COVID-19 generalization from one source to another. The segmentation achieved a Jaccard distance of 0.034 and a Dice coefficient of 0.982. The classification using segmented images achieved an F1-Score of 0.88 for the multi-class setup, and 0.83 for COVID-19 identification. In the cross-dataset scenario, we obtained an F1-Score of 0.74 and an area under the ROC curve of 0.9 for COVID-19 identification using segmented images. Experiments support the conclusion that even after segmentation, there is a strong bias introduced by underlying factors from different sources. |
format | Online Article Text |
id | pubmed-8587284 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-85872842021-11-13 Impact of Lung Segmentation on the Diagnosis and Explanation of COVID-19 in Chest X-ray Images Teixeira, Lucas O. Pereira, Rodolfo M. Bertolini, Diego Oliveira, Luiz S. Nanni, Loris Cavalcanti, George D. C. Costa, Yandre M. G. Sensors (Basel) Article COVID-19 frequently provokes pneumonia, which can be diagnosed using imaging exams. Chest X-ray (CXR) is often useful because it is cheap, fast, widespread, and uses less radiation. Here, we demonstrate the impact of lung segmentation in COVID-19 identification using CXR images and evaluate which contents of the image influenced the most. Semantic segmentation was performed using a U-Net CNN architecture, and the classification using three CNN architectures (VGG, ResNet, and Inception). Explainable Artificial Intelligence techniques were employed to estimate the impact of segmentation. A three-classes database was composed: lung opacity (pneumonia), COVID-19, and normal. We assessed the impact of creating a CXR image database from different sources, and the COVID-19 generalization from one source to another. The segmentation achieved a Jaccard distance of 0.034 and a Dice coefficient of 0.982. The classification using segmented images achieved an F1-Score of 0.88 for the multi-class setup, and 0.83 for COVID-19 identification. In the cross-dataset scenario, we obtained an F1-Score of 0.74 and an area under the ROC curve of 0.9 for COVID-19 identification using segmented images. Experiments support the conclusion that even after segmentation, there is a strong bias introduced by underlying factors from different sources. MDPI 2021-10-27 /pmc/articles/PMC8587284/ /pubmed/34770423 http://dx.doi.org/10.3390/s21217116 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Teixeira, Lucas O. Pereira, Rodolfo M. Bertolini, Diego Oliveira, Luiz S. Nanni, Loris Cavalcanti, George D. C. Costa, Yandre M. G. Impact of Lung Segmentation on the Diagnosis and Explanation of COVID-19 in Chest X-ray Images |
title | Impact of Lung Segmentation on the Diagnosis and Explanation of COVID-19 in Chest X-ray Images |
title_full | Impact of Lung Segmentation on the Diagnosis and Explanation of COVID-19 in Chest X-ray Images |
title_fullStr | Impact of Lung Segmentation on the Diagnosis and Explanation of COVID-19 in Chest X-ray Images |
title_full_unstemmed | Impact of Lung Segmentation on the Diagnosis and Explanation of COVID-19 in Chest X-ray Images |
title_short | Impact of Lung Segmentation on the Diagnosis and Explanation of COVID-19 in Chest X-ray Images |
title_sort | impact of lung segmentation on the diagnosis and explanation of covid-19 in chest x-ray images |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8587284/ https://www.ncbi.nlm.nih.gov/pubmed/34770423 http://dx.doi.org/10.3390/s21217116 |
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