{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,13]],"date-time":"2026-03-13T04:35:53Z","timestamp":1773376553883,"version":"3.50.1"},"reference-count":48,"publisher":"MDPI AG","issue":"21","license":[{"start":{"date-parts":[[2021,10,27]],"date-time":"2021-10-27T00:00:00Z","timestamp":1635292800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100002322","name":"Coordena\u00e7\u00e3o de Aperfeicoamento de Pessoal de N\u00edvel Superior","doi-asserted-by":"publisher","award":["88887.630259\/2021-00"],"award-info":[{"award-number":["88887.630259\/2021-00"]}],"id":[{"id":"10.13039\/501100002322","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003593","name":"National Council for Scientific and Technological Development","doi-asserted-by":"publisher","award":["163458\/2020-0"],"award-info":[{"award-number":["163458\/2020-0"]}],"id":[{"id":"10.13039\/501100003593","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003593","name":"National Council for Scientific and Technological Development","doi-asserted-by":"publisher","award":["312672\/2020-9"],"award-info":[{"award-number":["312672\/2020-9"]}],"id":[{"id":"10.13039\/501100003593","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>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.<\/jats:p>","DOI":"10.3390\/s21217116","type":"journal-article","created":{"date-parts":[[2021,10,27]],"date-time":"2021-10-27T23:24:42Z","timestamp":1635377082000},"page":"7116","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":114,"title":["Impact of Lung Segmentation on the Diagnosis and Explanation of COVID-19 in Chest X-ray Images"],"prefix":"10.3390","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3615-1567","authenticated-orcid":false,"given":"Lucas O.","family":"Teixeira","sequence":"first","affiliation":[{"name":"Departamento de Inform\u00e1tica, Universidade Estadual de Maring\u00e1, Maring\u00e1 87020-900, Brazil"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1272-5378","authenticated-orcid":false,"given":"Rodolfo M.","family":"Pereira","sequence":"additional","affiliation":[{"name":"Instituto Federal do Paran\u00e1, Pinhais 83330-200, Brazil"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6196-4538","authenticated-orcid":false,"given":"Diego","family":"Bertolini","sequence":"additional","affiliation":[{"name":"Departamento Acad\u00eamico de Ci\u00eancia da Computa\u00e7\u00e3o, Universidade Tecnol\u00f3gica Federal do Paran\u00e1, Campo Mour\u00e3o 87301-899, Brazil"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0595-5370","authenticated-orcid":false,"given":"Luiz S.","family":"Oliveira","sequence":"additional","affiliation":[{"name":"Departamento de Inform\u00e1tica, Universidade Federal do Paran\u00e1, Curitiba 81531-980, Brazil"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3502-7209","authenticated-orcid":false,"given":"Loris","family":"Nanni","sequence":"additional","affiliation":[{"name":"Dipartimento di Ingegneria dell\u2019Informazione, Universit\u00e0 degli Studi di Padova, 35122 Padova, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7714-2283","authenticated-orcid":false,"given":"George D. C.","family":"Cavalcanti","sequence":"additional","affiliation":[{"name":"Centro de Inform\u00e1tica, Universidade Federal de Pernambuco, Recife 50740-560, Brazil"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0630-3171","authenticated-orcid":false,"given":"Yandre M. G.","family":"Costa","sequence":"additional","affiliation":[{"name":"Departamento de Inform\u00e1tica, Universidade Estadual de Maring\u00e1, Maring\u00e1 87020-900, Brazil"}]}],"member":"1968","published-online":{"date-parts":[[2021,10,27]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"363","DOI":"10.1038\/s41577-020-0311-8","article-title":"The trinity of COVID-19: Immunity, inflammation and intervention","volume":"20","author":"Tay","year":"2020","journal-title":"Nat. Rev. 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