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://dx.doi.org/10.1109/IJCNN.2018.8489199 http://hdl.handle.net/11449/232818 |
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.Department of Computing São Paulo State UniversityFederal Institute of Education Science and Technology of Ceará CEGraduate Program in Applied Informatics University of FortalezaDepartment of Computing São Paulo State UniversityUniversidade Estadual Paulista (UNESP)CEUniversity of FortalezaPereira, Danillo Roberto [UNESP]Filho, Pedro P. RebouçasDe Rosa, Gustavo Henrique [UNESP]Papa, João Paulo [UNESP]De Albuquerque, Victor Hugo C.2022-04-30T12:56:16Z2022-04-30T12:56:16Z2018-10-10info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/conferenceObjecthttp://dx.doi.org/10.1109/IJCNN.2018.8489199Proceedings of the International Joint Conference on Neural Networks, v. 2018-July.http://hdl.handle.net/11449/23281810.1109/IJCNN.2018.84891992-s2.0-85056513830Scopusreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengProceedings of the International Joint Conference on Neural Networksinfo:eu-repo/semantics/openAccess2024-04-23T16:11:34Zoai:repositorio.unesp.br:11449/232818Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-08-05T23:35:14.846281Repositório 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] Filho, Pedro P. Rebouças De Rosa, Gustavo Henrique [UNESP] Papa, João Paulo [UNESP] De Albuquerque, Victor Hugo C. |
author_role |
author |
author2 |
Filho, Pedro P. Rebouças De Rosa, Gustavo Henrique [UNESP] Papa, João Paulo [UNESP] De Albuquerque, Victor Hugo C. |
author2_role |
author author author author |
dc.contributor.none.fl_str_mv |
Universidade Estadual Paulista (UNESP) CE University of Fortaleza |
dc.contributor.author.fl_str_mv |
Pereira, Danillo Roberto [UNESP] Filho, Pedro P. Rebouças De Rosa, Gustavo Henrique [UNESP] Papa, João Paulo [UNESP] De Albuquerque, Victor Hugo C. |
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 |
2018-10-10 2022-04-30T12:56:16Z 2022-04-30T12:56:16Z |
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 |
http://dx.doi.org/10.1109/IJCNN.2018.8489199 Proceedings of the International Joint Conference on Neural Networks, v. 2018-July. http://hdl.handle.net/11449/232818 10.1109/IJCNN.2018.8489199 2-s2.0-85056513830 |
url |
http://dx.doi.org/10.1109/IJCNN.2018.8489199 http://hdl.handle.net/11449/232818 |
identifier_str_mv |
Proceedings of the International Joint Conference on Neural Networks, v. 2018-July. 10.1109/IJCNN.2018.8489199 2-s2.0-85056513830 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Proceedings of the International Joint Conference on Neural Networks |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
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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1808129533348413440 |