Stroke Lesion Detection Using Convolutional Neural Networks
Autor(a) principal: | |
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Data de Publicação: | 2018 |
Outros Autores: | , , , , |
Tipo de documento: | Artigo de conferência |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UNESP |
Texto Completo: | http://hdl.handle.net/11449/209622 |
Resumo: | Stroke is an injury that affects the brain tissue, mainly caused by changes in the blood supply to a particular region of the brain. As consequence, some specific functions related to that affected region can be reduced, decreasing the quality of life of the patient. In this work, we deal with the problem of stroke detection in Computed Tomography (CT) images using Convolutional Neural Networks (CNN) optimized by Particle Swarm Optimization (PSO). We considered two different kinds of strokes, ischemic and hemorrhagic, as well as making available a public dataset to foster the research related to stroke detection in the human brain. The dataset comprises three different types of images for each case, i.e., the original CT image, one with the segmented cranium and an additional one with the radiological density's map. The results evidenced that CNN's are suitable to deal with stroke detection, obtaining promising results. |
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Repositório Institucional da UNESP |
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spelling |
Stroke Lesion Detection Using Convolutional Neural NetworksStroke is an injury that affects the brain tissue, mainly caused by changes in the blood supply to a particular region of the brain. As consequence, some specific functions related to that affected region can be reduced, decreasing the quality of life of the patient. In this work, we deal with the problem of stroke detection in Computed Tomography (CT) images using Convolutional Neural Networks (CNN) optimized by Particle Swarm Optimization (PSO). We considered two different kinds of strokes, ischemic and hemorrhagic, as well as making available a public dataset to foster the research related to stroke detection in the human brain. The dataset comprises three different types of images for each case, i.e., the original CT image, one with the segmented cranium and an additional one with the radiological density's map. The results evidenced that CNN's are suitable to deal with stroke detection, obtaining promising results.Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)Fundação para o Desenvolvimento da UNESP (FUNDUNESP)Sao Paulo State Univ, Dept Comp, Bauru, SP, BrazilFed Inst Educ Sci & Technol Ceara, Limoeiro Do Norte, CE, BrazilUniv Fortaleza, Grad Program Appl Informat, Fortaleza, CE, BrazilSao Paulo State Univ, Dept Comp, Bauru, SP, BrazilFAPESP: 2013/073750FAPESP: 2014/12236-1FAPESP: 2014/16250-9FAPESP: 2015/25739-4FAPESP: 2016/21243-7CNPq: 470501/2013-8CNPq: 301928/2014-2CNPq: 306166/2014-3CNPq: 307066/2017-7FUNDUNESP: 2597.2017IeeeUniversidade Estadual Paulista (Unesp)Fed Inst Educ Sci & Technol CearaUniv FortalezaPereira, Danillo Roberto [UNESP]Reboucas Filho, Pedro P.Rosa, Gustavo Henrique de [UNESP]Papa, Joao Paulo [UNESP]Albuquerque, Victor Hugo C. deIEEE2021-06-25T12:24:11Z2021-06-25T12:24:11Z2018-01-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/conferenceObject62018 International Joint Conference On Neural Networks (ijcnn). New York: Ieee, 6 p., 2018.2161-4393http://hdl.handle.net/11449/209622WOS:000585967402038Web of Sciencereponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPeng2018 International Joint Conference On Neural Networks (ijcnn)info:eu-repo/semantics/openAccess2024-04-23T16:11:28Zoai:repositorio.unesp.br:11449/209622Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-04-23T16:11:28Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false |
dc.title.none.fl_str_mv |
Stroke Lesion Detection Using Convolutional Neural Networks |
title |
Stroke Lesion Detection Using Convolutional Neural Networks |
spellingShingle |
Stroke Lesion Detection Using Convolutional Neural Networks Pereira, Danillo Roberto [UNESP] |
title_short |
Stroke Lesion Detection Using Convolutional Neural Networks |
title_full |
Stroke Lesion Detection Using Convolutional Neural Networks |
title_fullStr |
Stroke Lesion Detection Using Convolutional Neural Networks |
title_full_unstemmed |
Stroke Lesion Detection Using Convolutional Neural Networks |
title_sort |
Stroke Lesion Detection Using Convolutional Neural Networks |
author |
Pereira, Danillo Roberto [UNESP] |
author_facet |
Pereira, Danillo Roberto [UNESP] Reboucas Filho, Pedro P. Rosa, Gustavo Henrique de [UNESP] Papa, Joao Paulo [UNESP] Albuquerque, Victor Hugo C. de IEEE |
author_role |
author |
author2 |
Reboucas Filho, Pedro P. Rosa, Gustavo Henrique de [UNESP] Papa, Joao Paulo [UNESP] Albuquerque, Victor Hugo C. de IEEE |
author2_role |
author author author author author |
dc.contributor.none.fl_str_mv |
Universidade Estadual Paulista (Unesp) Fed Inst Educ Sci & Technol Ceara Univ Fortaleza |
dc.contributor.author.fl_str_mv |
Pereira, Danillo Roberto [UNESP] Reboucas Filho, Pedro P. Rosa, Gustavo Henrique de [UNESP] Papa, Joao Paulo [UNESP] Albuquerque, Victor Hugo C. de IEEE |
description |
Stroke is an injury that affects the brain tissue, mainly caused by changes in the blood supply to a particular region of the brain. As consequence, some specific functions related to that affected region can be reduced, decreasing the quality of life of the patient. In this work, we deal with the problem of stroke detection in Computed Tomography (CT) images using Convolutional Neural Networks (CNN) optimized by Particle Swarm Optimization (PSO). We considered two different kinds of strokes, ischemic and hemorrhagic, as well as making available a public dataset to foster the research related to stroke detection in the human brain. The dataset comprises three different types of images for each case, i.e., the original CT image, one with the segmented cranium and an additional one with the radiological density's map. The results evidenced that CNN's are suitable to deal with stroke detection, obtaining promising results. |
publishDate |
2018 |
dc.date.none.fl_str_mv |
2018-01-01 2021-06-25T12:24:11Z 2021-06-25T12:24:11Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/conferenceObject |
format |
conferenceObject |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
2018 International Joint Conference On Neural Networks (ijcnn). New York: Ieee, 6 p., 2018. 2161-4393 http://hdl.handle.net/11449/209622 WOS:000585967402038 |
identifier_str_mv |
2018 International Joint Conference On Neural Networks (ijcnn). New York: Ieee, 6 p., 2018. 2161-4393 WOS:000585967402038 |
url |
http://hdl.handle.net/11449/209622 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
2018 International Joint Conference On Neural Networks (ijcnn) |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
6 |
dc.publisher.none.fl_str_mv |
Ieee |
publisher.none.fl_str_mv |
Ieee |
dc.source.none.fl_str_mv |
Web of Science 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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1799965416352645120 |