Cerebral ischemia detection using Deep Learning techniques

Cerebrovascular accident (CVA), commonly known as stroke, stands as a significant contributor to contemporary mortality and morbidity rates, often leading to lasting disabilities. Early identification is crucial in mitigating its impact and reducing mortality. Non-contrast computed tomography (NCCT)...

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Autores: Pastor Vargas, Rafael, Antón‑Munárriz, Cristina, Haut, Juan M., Robles Gómez, Antonio, Paoletti, Mercedes E., Benítez Andrades, José Alberto
Tipo de recurso: artículo
Fecha de publicación:2025
País:España
Institución:Universidad Nacional de Educación a Distancia
Repositorio:e-spacio. Repositorio Institucional de la UNED
Idioma:inglés
OAI Identifier:oai:e-spacio.uned.es:20.500.14468/26841
Acceso en línea:https://hdl.handle.net/20.500.14468/26841
Access Level:acceso abierto
Palabra clave:1203.17 Informática
Cerebral ischemia
Computed tomography
Deep learning
Transfer learning
Ictus dataset
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spelling Cerebral ischemia detection using Deep Learning techniquesPastor Vargas, RafaelAntón‑Munárriz, CristinaHaut, Juan M.Robles Gómez, AntonioPaoletti, Mercedes E.Benítez Andrades, José Alberto1203.17 InformáticaCerebral ischemiaComputed tomographyDeep learningTransfer learningIctus datasetCerebrovascular accident (CVA), commonly known as stroke, stands as a significant contributor to contemporary mortality and morbidity rates, often leading to lasting disabilities. Early identification is crucial in mitigating its impact and reducing mortality. Non-contrast computed tomography (NCCT) remains the primary diagnostic tool in stroke emergencies due to its speed, accessibility, and cost-effectiveness. NCCT enables the exclusion of hemorrhage and directs attention to ischemic causes resulting from arterial flow obstruction. Quantification of NCCT findings employs the Alberta Stroke Program Early Computed Tomography Score (ASPECTS), which evaluates affected brain structures. This study seeks to identify early alterations in NCCT density in patients with stroke symptoms using a binary classifier distinguishing NCCT scans with and without stroke. To achieve this, various well-known deep learning architectures, namely VGG3D, ResNet3D, and DenseNet3D, validated in the ImageNet challenges, are implemented with 3D images covering the entire brain volume. The training results of these networks are presented, wherein diverse parameters are examined for optimal performance. The DenseNet3D network emerges as the most effective model, attaining a training set accuracy of 98% and a test set accuracy of 95%. The aim is to alert medical professionals to potential stroke cases in their early stages based on NCCT findings displaying altered density patterns.Springerhttps://orcid.org/0000-0002-4089-9538e-Spacio UNED20252025-05-2620252025-05-2020252025-05-20journal articlehttp://purl.org/coar/resource_type/c_6501info:eu-repo/semantics/articleapplication/pdfhttps://hdl.handle.net/20.500.14468/26841reponame:e-spacio. Repositorio Institucional de la UNEDinstname:Universidad Nacional de Educación a DistanciaInglésengopen accesshttp://purl.org/coar/access_right/c_abf2info:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by/4.0/deed.esoai:e-spacio.uned.es:20.500.14468/268412026-06-06T12:38:31Z
dc.title.none.fl_str_mv Cerebral ischemia detection using Deep Learning techniques
title Cerebral ischemia detection using Deep Learning techniques
spellingShingle Cerebral ischemia detection using Deep Learning techniques
Pastor Vargas, Rafael
1203.17 Informática
Cerebral ischemia
Computed tomography
Deep learning
Transfer learning
Ictus dataset
title_short Cerebral ischemia detection using Deep Learning techniques
title_full Cerebral ischemia detection using Deep Learning techniques
title_fullStr Cerebral ischemia detection using Deep Learning techniques
title_full_unstemmed Cerebral ischemia detection using Deep Learning techniques
title_sort Cerebral ischemia detection using Deep Learning techniques
dc.creator.none.fl_str_mv Pastor Vargas, Rafael
Antón‑Munárriz, Cristina
Haut, Juan M.
Robles Gómez, Antonio
Paoletti, Mercedes E.
Benítez Andrades, José Alberto
author Pastor Vargas, Rafael
author_facet Pastor Vargas, Rafael
Antón‑Munárriz, Cristina
Haut, Juan M.
Robles Gómez, Antonio
Paoletti, Mercedes E.
Benítez Andrades, José Alberto
author_role author
author2 Antón‑Munárriz, Cristina
Haut, Juan M.
Robles Gómez, Antonio
Paoletti, Mercedes E.
Benítez Andrades, José Alberto
author2_role author
author
author
author
author
dc.contributor.none.fl_str_mv https://orcid.org/0000-0002-4089-9538
e-Spacio UNED
dc.subject.none.fl_str_mv 1203.17 Informática
Cerebral ischemia
Computed tomography
Deep learning
Transfer learning
Ictus dataset
topic 1203.17 Informática
Cerebral ischemia
Computed tomography
Deep learning
Transfer learning
Ictus dataset
description Cerebrovascular accident (CVA), commonly known as stroke, stands as a significant contributor to contemporary mortality and morbidity rates, often leading to lasting disabilities. Early identification is crucial in mitigating its impact and reducing mortality. Non-contrast computed tomography (NCCT) remains the primary diagnostic tool in stroke emergencies due to its speed, accessibility, and cost-effectiveness. NCCT enables the exclusion of hemorrhage and directs attention to ischemic causes resulting from arterial flow obstruction. Quantification of NCCT findings employs the Alberta Stroke Program Early Computed Tomography Score (ASPECTS), which evaluates affected brain structures. This study seeks to identify early alterations in NCCT density in patients with stroke symptoms using a binary classifier distinguishing NCCT scans with and without stroke. To achieve this, various well-known deep learning architectures, namely VGG3D, ResNet3D, and DenseNet3D, validated in the ImageNet challenges, are implemented with 3D images covering the entire brain volume. The training results of these networks are presented, wherein diverse parameters are examined for optimal performance. The DenseNet3D network emerges as the most effective model, attaining a training set accuracy of 98% and a test set accuracy of 95%. The aim is to alert medical professionals to potential stroke cases in their early stages based on NCCT findings displaying altered density patterns.
publishDate 2025
dc.date.none.fl_str_mv 2025
2025-05-26
2025
2025-05-20
2025
2025-05-20
dc.type.none.fl_str_mv journal article
http://purl.org/coar/resource_type/c_6501
dc.type.openaire.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv https://hdl.handle.net/20.500.14468/26841
url https://hdl.handle.net/20.500.14468/26841
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
info:eu-repo/semantics/openAccess
http://creativecommons.org/licenses/by/4.0/deed.es
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2
http://creativecommons.org/licenses/by/4.0/deed.es
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Springer
publisher.none.fl_str_mv Springer
dc.source.none.fl_str_mv reponame:e-spacio. Repositorio Institucional de la UNED
instname:Universidad Nacional de Educación a Distancia
instname_str Universidad Nacional de Educación a Distancia
reponame_str e-spacio. Repositorio Institucional de la UNED
collection e-spacio. Repositorio Institucional de la UNED
repository.name.fl_str_mv
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