Brain Tumor Detection Using Magnetic Resonance Imaging and Convolutional Neural Networks

Early and precise detection of brain tumors is critical for improving clinical outcomes and patient quality of life. This research focused on developing an image classifier using convolutional neural networks (CNN) to detect brain tumors in magnetic resonance imaging (MRI). Brain tumors are a signif...

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Detalhes bibliográficos
Autores: Martínez-Del-Río-Ortega, Rafael, Civit Masot, Javier, Luna Perejón, Francisco, Domínguez Morales, Manuel Jesús
Formato: artículo
Estado:Versión publicada
Fecha de publicación:2024
País:España
Recursos:Universidad de Sevilla (US)
Repositorio:idUS. Depósito de Investigación de la Universidad de Sevilla
OAI Identifier:oai:idus.us.es:11441/167437
Acesso em linha:https://hdl.handle.net/11441/167437
https://doi.org/10.3390/bdcc8090123
Access Level:acceso abierto
Palavra-chave:Brain tumors
MRI
Convolutional neural networks
Deep learning
Image classification
Medical imaging
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spelling Brain Tumor Detection Using Magnetic Resonance Imaging and Convolutional Neural NetworksMartínez-Del-Río-Ortega, RafaelCivit Masot, JavierLuna Perejón, FranciscoDomínguez Morales, Manuel JesúsBrain tumorsMRIConvolutional neural networksDeep learningImage classificationMedical imagingEarly and precise detection of brain tumors is critical for improving clinical outcomes and patient quality of life. This research focused on developing an image classifier using convolutional neural networks (CNN) to detect brain tumors in magnetic resonance imaging (MRI). Brain tumors are a significant cause of morbidity and mortality worldwide, with approximately 300,000 new cases diagnosed annually. Magnetic resonance imaging (MRI) offers excellent spatial resolution and soft tissue contrast, making it indispensable for identifying brain abnormalities. However, accurate interpretation of MRI scans remains challenging, due to human subjectivity and variability in tumor appearance. This study employed CNNs, which have demonstrated exceptional performance in medical image analysis, to address these challenges. Various CNN architectures were implemented and evaluated to optimize brain tumor detection. The best model achieved an accuracy of 97.5%, sensitivity of 99.2%, and binary accuracy of 98.2%, surpassing previous studies. These results underscore the potential of deep learning techniques in clinical applications, significantly enhancing diagnostic accuracy and reliability.MDPIArquitectura y Tecnología de Computadores2024info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfapplication/pdfhttps://hdl.handle.net/11441/167437https://doi.org/10.3390/bdcc8090123reponame:idUS. Depósito de Investigación de la Universidad de Sevillainstname:Universidad de Sevilla (US)InglésBig Data and Cognitive Computing, 8 (9), 123.https://www.mdpi.com/2504-2289/8/9/123https://www.mdpi.com/2504-2289/8/9/123info:eu-repo/semantics/openAccessoai:idus.us.es:11441/1674372026-06-17T12:51:07Z
dc.title.none.fl_str_mv Brain Tumor Detection Using Magnetic Resonance Imaging and Convolutional Neural Networks
title Brain Tumor Detection Using Magnetic Resonance Imaging and Convolutional Neural Networks
spellingShingle Brain Tumor Detection Using Magnetic Resonance Imaging and Convolutional Neural Networks
Martínez-Del-Río-Ortega, Rafael
Brain tumors
MRI
Convolutional neural networks
Deep learning
Image classification
Medical imaging
title_short Brain Tumor Detection Using Magnetic Resonance Imaging and Convolutional Neural Networks
title_full Brain Tumor Detection Using Magnetic Resonance Imaging and Convolutional Neural Networks
title_fullStr Brain Tumor Detection Using Magnetic Resonance Imaging and Convolutional Neural Networks
title_full_unstemmed Brain Tumor Detection Using Magnetic Resonance Imaging and Convolutional Neural Networks
title_sort Brain Tumor Detection Using Magnetic Resonance Imaging and Convolutional Neural Networks
dc.creator.none.fl_str_mv Martínez-Del-Río-Ortega, Rafael
Civit Masot, Javier
Luna Perejón, Francisco
Domínguez Morales, Manuel Jesús
author Martínez-Del-Río-Ortega, Rafael
author_facet Martínez-Del-Río-Ortega, Rafael
Civit Masot, Javier
Luna Perejón, Francisco
Domínguez Morales, Manuel Jesús
author_role author
author2 Civit Masot, Javier
Luna Perejón, Francisco
Domínguez Morales, Manuel Jesús
author2_role author
author
author
dc.contributor.none.fl_str_mv Arquitectura y Tecnología de Computadores
dc.subject.none.fl_str_mv Brain tumors
MRI
Convolutional neural networks
Deep learning
Image classification
Medical imaging
topic Brain tumors
MRI
Convolutional neural networks
Deep learning
Image classification
Medical imaging
description Early and precise detection of brain tumors is critical for improving clinical outcomes and patient quality of life. This research focused on developing an image classifier using convolutional neural networks (CNN) to detect brain tumors in magnetic resonance imaging (MRI). Brain tumors are a significant cause of morbidity and mortality worldwide, with approximately 300,000 new cases diagnosed annually. Magnetic resonance imaging (MRI) offers excellent spatial resolution and soft tissue contrast, making it indispensable for identifying brain abnormalities. However, accurate interpretation of MRI scans remains challenging, due to human subjectivity and variability in tumor appearance. This study employed CNNs, which have demonstrated exceptional performance in medical image analysis, to address these challenges. Various CNN architectures were implemented and evaluated to optimize brain tumor detection. The best model achieved an accuracy of 97.5%, sensitivity of 99.2%, and binary accuracy of 98.2%, surpassing previous studies. These results underscore the potential of deep learning techniques in clinical applications, significantly enhancing diagnostic accuracy and reliability.
publishDate 2024
dc.date.none.fl_str_mv 2024
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv https://hdl.handle.net/11441/167437
https://doi.org/10.3390/bdcc8090123
url https://hdl.handle.net/11441/167437
https://doi.org/10.3390/bdcc8090123
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv Big Data and Cognitive Computing, 8 (9), 123.
https://www.mdpi.com/2504-2289/8/9/123
https://www.mdpi.com/2504-2289/8/9/123
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv MDPI
publisher.none.fl_str_mv MDPI
dc.source.none.fl_str_mv reponame:idUS. Depósito de Investigación de la Universidad de Sevilla
instname:Universidad de Sevilla (US)
instname_str Universidad de Sevilla (US)
reponame_str idUS. Depósito de Investigación de la Universidad de Sevilla
collection idUS. Depósito de Investigación de la Universidad de Sevilla
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15,811543

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