Towards better heartbeat segmentation with deep learning classification.

Purpose Confronting the pandemic of COVID-19 is nowadays one of the most prominent challenges of the human species. A key factor in slowing down the virus propagation is the rapid diagnosis and isolation of infected patients. The standard method for COVID-19 identification, the Reverse transcription...

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Autores: Luz, Eduardo José da Silva, Silva, Pedro Henrique Lopes, Silva, Rodrigo César Pedrosa, Silva, Ludmila, Guimarães, João, Miozzo, Gustavo, Moreira, Gladston Juliano Prates, Gomes, David Menotti
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2020
País:Brasil
Institución:Universidade Federal de Ouro Preto (UFOP)
Repositorio:Repositório Institucional da UFOP
Idioma:inglés
OAI Identifier:oai:repositorio.ufop.br:123456789/14451
Acceso en línea:http://www.repositorio.ufop.br/jspui/handle/123456789/14451
https://doi.org/10.1038/s41598-020-77745-0
Access Level:acceso abierto
Palabra clave:EfficientNet
Pneumonia
Chest (X-ray) radiography
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spelling Towards better heartbeat segmentation with deep learning classification.EfficientNetPneumoniaChest (X-ray) radiographyPurpose Confronting the pandemic of COVID-19 is nowadays one of the most prominent challenges of the human species. A key factor in slowing down the virus propagation is the rapid diagnosis and isolation of infected patients. The standard method for COVID-19 identification, the Reverse transcription polymerase chain reaction method, is time-consuming and in short supply due to the pandemic. Thus, researchers have been looking for alternative screening methods, and deep learning applied to chest X-rays of patients has been showing promising results. Despite their success, the computational cost of these methods remains high, which imposes difficulties to their accessibility and availability. Thus, the main goal of this work is to propose an accurate yet efficient method in terms of memory and processing time for the problem of COVID-19 screening in chest X-rays. Methods To achieve the defined objective, we propose a new family of models based on the EfficientNet family of deep artificial neural networks which are known for their high accuracy and low footprints. We also exploit the underlying taxonomy of the problem with a hierarchical classifier. A dataset of 13,569 X-ray images divided into healthy, non-COVID-19 pneumonia, and COVID-19 patients is used to train the proposed approaches and other 5 competing architectures. We also propose a cross-dataset evaluation with a second dataset to evaluate the method generalization power. Results The results show that the proposed approach was able to produce a high-quality model, with an overall accuracy of 93.9%, COVID-19 sensitivity of 96.8%, and positive prediction of 100% while having from 5 to 30 times fewer parameters than the other tested architectures. Larger and more heterogeneous databases are still needed for validation before claiming that deep learning can assist physicians in the task of detecting COVID-19 in X-ray images, since the cross-dataset evaluation shows that even state-of-the-art models suffer from a lack of generalization power. Conclusions We believe the reported figures represent state-of-the-art results, both in terms of efficiency and effectiveness, for the COVIDx database, a database of 13,800 X-ray images, 183 of which are from patients affected by COVID-19. The current proposal is a promising candidate for embedding in medical equipment or even physicians’ mobile phones.2022-02-07T19:40:29Z2022-02-07T19:40:29Z2020info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfLUZ, E. J. da S. et al. Towards better heartbeat segmentation with deep learning classification. Scientific Reports, v. 10, artigo 20701, 2020. Disponível em: <https://www.nature.com/articles/s41598-020-77745-0>. Acesso em: 25 agosto 2021.2045-2322http://www.repositorio.ufop.br/jspui/handle/123456789/14451https://doi.org/10.1038/s41598-020-77745-0ark:/61566/0013000005c85This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. Fonte: o PDF do artigo.info:eu-repo/semantics/openAccessengreponame:Repositório Institucional da UFOPinstname:Universidade Federal de Ouro Preto (UFOP)instacron:UFOPLuz, Eduardo José da SilvaSilva, Pedro Henrique LopesSilva, Rodrigo César PedrosaSilva, LudmilaGuimarães, JoãoMiozzo, GustavoMoreira, Gladston Juliano PratesGomes, David Menotti2024-11-10T16:35:50Zoai:repositorio.ufop.br:123456789/14451Repositório InstitucionalPUBhttp://www.repositorio.ufop.br/oai/requestrepositorio@ufop.edu.bropendoar:32332024-11-10T16:35:50Repositório Institucional da UFOP - Universidade Federal de Ouro Preto (UFOP)false
dc.title.none.fl_str_mv Towards better heartbeat segmentation with deep learning classification.
title Towards better heartbeat segmentation with deep learning classification.
spellingShingle Towards better heartbeat segmentation with deep learning classification.
Luz, Eduardo José da Silva
EfficientNet
Pneumonia
Chest (X-ray) radiography
title_short Towards better heartbeat segmentation with deep learning classification.
title_full Towards better heartbeat segmentation with deep learning classification.
title_fullStr Towards better heartbeat segmentation with deep learning classification.
title_full_unstemmed Towards better heartbeat segmentation with deep learning classification.
title_sort Towards better heartbeat segmentation with deep learning classification.
dc.creator.none.fl_str_mv Luz, Eduardo José da Silva
Silva, Pedro Henrique Lopes
Silva, Rodrigo César Pedrosa
Silva, Ludmila
Guimarães, João
Miozzo, Gustavo
Moreira, Gladston Juliano Prates
Gomes, David Menotti
author Luz, Eduardo José da Silva
author_facet Luz, Eduardo José da Silva
Silva, Pedro Henrique Lopes
Silva, Rodrigo César Pedrosa
Silva, Ludmila
Guimarães, João
Miozzo, Gustavo
Moreira, Gladston Juliano Prates
Gomes, David Menotti
author_role author
author2 Silva, Pedro Henrique Lopes
Silva, Rodrigo César Pedrosa
Silva, Ludmila
Guimarães, João
Miozzo, Gustavo
Moreira, Gladston Juliano Prates
Gomes, David Menotti
author2_role author
author
author
author
author
author
author
dc.subject.por.fl_str_mv EfficientNet
Pneumonia
Chest (X-ray) radiography
topic EfficientNet
Pneumonia
Chest (X-ray) radiography
description Purpose Confronting the pandemic of COVID-19 is nowadays one of the most prominent challenges of the human species. A key factor in slowing down the virus propagation is the rapid diagnosis and isolation of infected patients. The standard method for COVID-19 identification, the Reverse transcription polymerase chain reaction method, is time-consuming and in short supply due to the pandemic. Thus, researchers have been looking for alternative screening methods, and deep learning applied to chest X-rays of patients has been showing promising results. Despite their success, the computational cost of these methods remains high, which imposes difficulties to their accessibility and availability. Thus, the main goal of this work is to propose an accurate yet efficient method in terms of memory and processing time for the problem of COVID-19 screening in chest X-rays. Methods To achieve the defined objective, we propose a new family of models based on the EfficientNet family of deep artificial neural networks which are known for their high accuracy and low footprints. We also exploit the underlying taxonomy of the problem with a hierarchical classifier. A dataset of 13,569 X-ray images divided into healthy, non-COVID-19 pneumonia, and COVID-19 patients is used to train the proposed approaches and other 5 competing architectures. We also propose a cross-dataset evaluation with a second dataset to evaluate the method generalization power. Results The results show that the proposed approach was able to produce a high-quality model, with an overall accuracy of 93.9%, COVID-19 sensitivity of 96.8%, and positive prediction of 100% while having from 5 to 30 times fewer parameters than the other tested architectures. Larger and more heterogeneous databases are still needed for validation before claiming that deep learning can assist physicians in the task of detecting COVID-19 in X-ray images, since the cross-dataset evaluation shows that even state-of-the-art models suffer from a lack of generalization power. Conclusions We believe the reported figures represent state-of-the-art results, both in terms of efficiency and effectiveness, for the COVIDx database, a database of 13,800 X-ray images, 183 of which are from patients affected by COVID-19. The current proposal is a promising candidate for embedding in medical equipment or even physicians’ mobile phones.
publishDate 2020
dc.date.none.fl_str_mv 2020
2022-02-07T19:40:29Z
2022-02-07T19:40:29Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv LUZ, E. J. da S. et al. Towards better heartbeat segmentation with deep learning classification. Scientific Reports, v. 10, artigo 20701, 2020. Disponível em: <https://www.nature.com/articles/s41598-020-77745-0>. Acesso em: 25 agosto 2021.
2045-2322
http://www.repositorio.ufop.br/jspui/handle/123456789/14451
https://doi.org/10.1038/s41598-020-77745-0
dc.identifier.dark.fl_str_mv ark:/61566/0013000005c85
identifier_str_mv LUZ, E. J. da S. et al. Towards better heartbeat segmentation with deep learning classification. Scientific Reports, v. 10, artigo 20701, 2020. Disponível em: <https://www.nature.com/articles/s41598-020-77745-0>. Acesso em: 25 agosto 2021.
2045-2322
ark:/61566/0013000005c85
url http://www.repositorio.ufop.br/jspui/handle/123456789/14451
https://doi.org/10.1038/s41598-020-77745-0
dc.language.iso.fl_str_mv eng
language eng
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.source.none.fl_str_mv reponame:Repositório Institucional da UFOP
instname:Universidade Federal de Ouro Preto (UFOP)
instacron:UFOP
instname_str Universidade Federal de Ouro Preto (UFOP)
instacron_str UFOP
institution UFOP
reponame_str Repositório Institucional da UFOP
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repository.mail.fl_str_mv repositorio@ufop.edu.br
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