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Article

Impact of Lung Segmentation on the Diagnosis and Explanation of COVID-19 in Chest X-ray Images

1
Departamento de Informática, Universidade Estadual de Maringá, Maringá 87020-900, Brazil
2
Instituto Federal do Paraná, Pinhais 83330-200, Brazil
3
Departamento Acadêmico de Ciência da Computação, Universidade Tecnológica Federal do Paraná, Campo Mourão 87301-899, Brazil
4
Departamento de Informática, Universidade Federal do Paraná, Curitiba 81531-980, Brazil
5
Dipartimento di Ingegneria dell’Informazione, Università degli Studi di Padova, 35122 Padova, Italy
6
Centro de Informática, Universidade Federal de Pernambuco, Recife 50740-560, Brazil
*
Author to whom correspondence should be addressed.
Academic Editor: Christoph M. Friedrich
Sensors 2021, 21(21), 7116; https://doi.org/10.3390/s21217116
Received: 14 September 2021 / Revised: 19 October 2021 / Accepted: 21 October 2021 / Published: 27 October 2021
(This article belongs to the Special Issue Medical Image Classification)
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. View Full-Text
Keywords: COVID-19; chest X-ray; semantic segmentation; explainable artificial intelligence COVID-19; chest X-ray; semantic segmentation; explainable artificial intelligence
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MDPI and ACS Style

Teixeira, L.O.; Pereira, R.M.; Bertolini, D.; Oliveira, L.S.; Nanni, L.; Cavalcanti, G.D.C.; Costa, Y.M.G. Impact of Lung Segmentation on the Diagnosis and Explanation of COVID-19 in Chest X-ray Images. Sensors 2021, 21, 7116. https://doi.org/10.3390/s21217116

AMA Style

Teixeira LO, Pereira RM, Bertolini D, Oliveira LS, Nanni L, Cavalcanti GDC, Costa YMG. Impact of Lung Segmentation on the Diagnosis and Explanation of COVID-19 in Chest X-ray Images. Sensors. 2021; 21(21):7116. https://doi.org/10.3390/s21217116

Chicago/Turabian Style

Teixeira, Lucas O., Rodolfo M. Pereira, Diego Bertolini, Luiz S. Oliveira, Loris Nanni, George D.C. Cavalcanti, and Yandre M.G. Costa 2021. "Impact of Lung Segmentation on the Diagnosis and Explanation of COVID-19 in Chest X-ray Images" Sensors 21, no. 21: 7116. https://doi.org/10.3390/s21217116

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