Normalizing images is good to improve computer-assisted COVID-19 diagnosis
Autor(a) principal: | |
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Data de Publicação: | 2021 |
Outros Autores: | , , |
Tipo de documento: | Capítulo de livro |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UNESP |
Texto Completo: | http://dx.doi.org/10.1016/B978-0-12-824536-1.00033-2 http://hdl.handle.net/11449/234306 |
Resumo: | The Coronavirus Disease 2019 (COVID-19) outbreak, caused by the SARS-CoV-2 virus, surprised the whole world in an unprecedented and devastating way, resulting in almost deaths and 2.3 million infections worldwide in less than 4 months. Moreover, the elevate capability of transmission threatens to collapse both the healthy and economic systems from most countries, stressing worse predictions for emerging countries. In such a turbulent scenario, fast diagnosis is essential for a successful treatment and isolation of patients, thus avoiding increasing the number of contaminations. However, traditional methods of detection using polymerase chain reaction are impractical in large scale due to elevate costs, material scarcity, and time demanded for processing. As an alternative, some researchers proposed a machine learning-based diagnosis considering chest X-ray analysis with promising results, thus opening room for possible improvements. This work introduces a different normalization approach that, together with an EfficientNet-B6-inspired neural network, can deal with COVID-19 diagnosis considering chest X-ray images. Experiments provided competitive results considering a lighter and faster architecture, thus fostering research toward COVID-19 detection. |
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Repositório Institucional da UNESP |
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Normalizing images is good to improve computer-assisted COVID-19 diagnosisConvolutional neural networkCoronavirusCOVID-19The Coronavirus Disease 2019 (COVID-19) outbreak, caused by the SARS-CoV-2 virus, surprised the whole world in an unprecedented and devastating way, resulting in almost deaths and 2.3 million infections worldwide in less than 4 months. Moreover, the elevate capability of transmission threatens to collapse both the healthy and economic systems from most countries, stressing worse predictions for emerging countries. In such a turbulent scenario, fast diagnosis is essential for a successful treatment and isolation of patients, thus avoiding increasing the number of contaminations. However, traditional methods of detection using polymerase chain reaction are impractical in large scale due to elevate costs, material scarcity, and time demanded for processing. As an alternative, some researchers proposed a machine learning-based diagnosis considering chest X-ray analysis with promising results, thus opening room for possible improvements. This work introduces a different normalization approach that, together with an EfficientNet-B6-inspired neural network, can deal with COVID-19 diagnosis considering chest X-ray images. Experiments provided competitive results considering a lighter and faster architecture, thus fostering research toward COVID-19 detection.Department of Computing Federal University of São CarlosDepartment of Computing São Paulo State UniversityDepartment of Computing São Paulo State UniversityUniversidade Federal de São Carlos (UFSCar)Universidade Estadual Paulista (UNESP)Santos, Claudio Filipi GonçalvesdosPassos, Leandro Aparecido [UNESP]Santana, Marcos Cleisonde [UNESP]Papa, João Paulo [UNESP]2022-05-01T15:46:18Z2022-05-01T15:46:18Z2021-01-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/bookPart51-62http://dx.doi.org/10.1016/B978-0-12-824536-1.00033-2Data Science for COVID-19 Volume 1: Computational Perspectives, p. 51-62.http://hdl.handle.net/11449/23430610.1016/B978-0-12-824536-1.00033-22-s2.0-85126913352Scopusreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengData Science for COVID-19 Volume 1: Computational Perspectivesinfo:eu-repo/semantics/openAccess2022-05-01T15:46:18Zoai:repositorio.unesp.br:11449/234306Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462022-05-01T15:46:18Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false |
dc.title.none.fl_str_mv |
Normalizing images is good to improve computer-assisted COVID-19 diagnosis |
title |
Normalizing images is good to improve computer-assisted COVID-19 diagnosis |
spellingShingle |
Normalizing images is good to improve computer-assisted COVID-19 diagnosis Santos, Claudio Filipi Gonçalvesdos Convolutional neural network Coronavirus COVID-19 |
title_short |
Normalizing images is good to improve computer-assisted COVID-19 diagnosis |
title_full |
Normalizing images is good to improve computer-assisted COVID-19 diagnosis |
title_fullStr |
Normalizing images is good to improve computer-assisted COVID-19 diagnosis |
title_full_unstemmed |
Normalizing images is good to improve computer-assisted COVID-19 diagnosis |
title_sort |
Normalizing images is good to improve computer-assisted COVID-19 diagnosis |
author |
Santos, Claudio Filipi Gonçalvesdos |
author_facet |
Santos, Claudio Filipi Gonçalvesdos Passos, Leandro Aparecido [UNESP] Santana, Marcos Cleisonde [UNESP] Papa, João Paulo [UNESP] |
author_role |
author |
author2 |
Passos, Leandro Aparecido [UNESP] Santana, Marcos Cleisonde [UNESP] Papa, João Paulo [UNESP] |
author2_role |
author author author |
dc.contributor.none.fl_str_mv |
Universidade Federal de São Carlos (UFSCar) Universidade Estadual Paulista (UNESP) |
dc.contributor.author.fl_str_mv |
Santos, Claudio Filipi Gonçalvesdos Passos, Leandro Aparecido [UNESP] Santana, Marcos Cleisonde [UNESP] Papa, João Paulo [UNESP] |
dc.subject.por.fl_str_mv |
Convolutional neural network Coronavirus COVID-19 |
topic |
Convolutional neural network Coronavirus COVID-19 |
description |
The Coronavirus Disease 2019 (COVID-19) outbreak, caused by the SARS-CoV-2 virus, surprised the whole world in an unprecedented and devastating way, resulting in almost deaths and 2.3 million infections worldwide in less than 4 months. Moreover, the elevate capability of transmission threatens to collapse both the healthy and economic systems from most countries, stressing worse predictions for emerging countries. In such a turbulent scenario, fast diagnosis is essential for a successful treatment and isolation of patients, thus avoiding increasing the number of contaminations. However, traditional methods of detection using polymerase chain reaction are impractical in large scale due to elevate costs, material scarcity, and time demanded for processing. As an alternative, some researchers proposed a machine learning-based diagnosis considering chest X-ray analysis with promising results, thus opening room for possible improvements. This work introduces a different normalization approach that, together with an EfficientNet-B6-inspired neural network, can deal with COVID-19 diagnosis considering chest X-ray images. Experiments provided competitive results considering a lighter and faster architecture, thus fostering research toward COVID-19 detection. |
publishDate |
2021 |
dc.date.none.fl_str_mv |
2021-01-01 2022-05-01T15:46:18Z 2022-05-01T15:46:18Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/bookPart |
format |
bookPart |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://dx.doi.org/10.1016/B978-0-12-824536-1.00033-2 Data Science for COVID-19 Volume 1: Computational Perspectives, p. 51-62. http://hdl.handle.net/11449/234306 10.1016/B978-0-12-824536-1.00033-2 2-s2.0-85126913352 |
url |
http://dx.doi.org/10.1016/B978-0-12-824536-1.00033-2 http://hdl.handle.net/11449/234306 |
identifier_str_mv |
Data Science for COVID-19 Volume 1: Computational Perspectives, p. 51-62. 10.1016/B978-0-12-824536-1.00033-2 2-s2.0-85126913352 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Data Science for COVID-19 Volume 1: Computational Perspectives |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
51-62 |
dc.source.none.fl_str_mv |
Scopus reponame:Repositório Institucional da UNESP instname:Universidade Estadual Paulista (UNESP) instacron:UNESP |
instname_str |
Universidade Estadual Paulista (UNESP) |
instacron_str |
UNESP |
institution |
UNESP |
reponame_str |
Repositório Institucional da UNESP |
collection |
Repositório Institucional da UNESP |
repository.name.fl_str_mv |
Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP) |
repository.mail.fl_str_mv |
|
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1792962176219086848 |