Noninvasive Grading of Glioma Tumor Using Magnetic Resonance Imaging with Convolutional Neural Networks

In recent years, Convolutional Neural Networks (ConvNets) have rapidly emerged as a widespread machine learning technique in a number of applications especially in the area of medical image classification and segmentation. In this paper, we propose a novel approach that uses ConvNet for classifying...

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Detalhes bibliográficos
Autores: Khawaldeh, Saed, Pervaiz, Usama, Rafiq, Azhar, Alkhawaldeh, Rami S.
Formato: artículo
Estado:Versión publicada
Fecha de publicación:2017
País:España
Recursos:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
Repositorio:Recercat. Dipósit de la Recerca de Catalunya
OAI Identifier:oai:recercat.cat:10256/18026
Acesso em linha:http://hdl.handle.net/10256/18026
Access Level:acceso abierto
Palavra-chave:Imatge -- Segmentació
Imaging segmentation
Cervell -- Imatgeria per ressonància magnètica
Brain -- Magnetic resonance imaging
Imatgeria mèdica
Imaging systems in medicine
Cervell -- Tumors
Brain -- Tumors
Glioblastoma multiforme
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oai_identifier_str oai:recercat.cat:10256/18026
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network_name_str España
repository_id_str
spelling Noninvasive Grading of Glioma Tumor Using Magnetic Resonance Imaging with Convolutional Neural NetworksKhawaldeh, SaedPervaiz, UsamaRafiq, AzharAlkhawaldeh, Rami S.Imatge -- SegmentacióImaging segmentationCervell -- Imatgeria per ressonància magnèticaBrain -- Magnetic resonance imagingImatgeria mèdicaImaging systems in medicineCervell -- TumorsBrain -- TumorsGlioblastoma multiformeGlioblastoma multiformeIn recent years, Convolutional Neural Networks (ConvNets) have rapidly emerged as a widespread machine learning technique in a number of applications especially in the area of medical image classification and segmentation. In this paper, we propose a novel approach that uses ConvNet for classifying brain medical images into healthy and unhealthy brain images. The unhealthy images of brain tumors are categorized also into low grades and high grades. In particular, we use the modified version of the Alex Krizhevsky network (AlexNet) deep learning architecture on magnetic resonance images as a potential tumor classification technique. The classification is performed on the whole image where the labels in the training set are at the image level rather than the pixel level. The results showed a reasonable performance in characterizing the brain medical images with an accuracy of 91.16%MDPI (Multidisciplinary Digital Publishing Institute)2017info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionpeer-reviewedapplication/pdfhttp://hdl.handle.net/10256/18026http://hdl.handle.net/10256/18026Applied Sciences, 2018, vol. 8, núm. 1, p. 27Articles publicats (D-ATC)reponame:Recercat. Dipósit de la Recerca de Catalunyainstname:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)Inglésinfo:eu-repo/semantics/altIdentifier/doi/10.3390/app8010027info:eu-repo/semantics/altIdentifier/eissn/2076-3417Attribution 4.0 Internationalhttp://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccessoai:recercat.cat:10256/180262026-05-29T05:05:01Z
dc.title.none.fl_str_mv Noninvasive Grading of Glioma Tumor Using Magnetic Resonance Imaging with Convolutional Neural Networks
title Noninvasive Grading of Glioma Tumor Using Magnetic Resonance Imaging with Convolutional Neural Networks
spellingShingle Noninvasive Grading of Glioma Tumor Using Magnetic Resonance Imaging with Convolutional Neural Networks
Khawaldeh, Saed
Imatge -- Segmentació
Imaging segmentation
Cervell -- Imatgeria per ressonància magnètica
Brain -- Magnetic resonance imaging
Imatgeria mèdica
Imaging systems in medicine
Cervell -- Tumors
Brain -- Tumors
Glioblastoma multiforme
Glioblastoma multiforme
title_short Noninvasive Grading of Glioma Tumor Using Magnetic Resonance Imaging with Convolutional Neural Networks
title_full Noninvasive Grading of Glioma Tumor Using Magnetic Resonance Imaging with Convolutional Neural Networks
title_fullStr Noninvasive Grading of Glioma Tumor Using Magnetic Resonance Imaging with Convolutional Neural Networks
title_full_unstemmed Noninvasive Grading of Glioma Tumor Using Magnetic Resonance Imaging with Convolutional Neural Networks
title_sort Noninvasive Grading of Glioma Tumor Using Magnetic Resonance Imaging with Convolutional Neural Networks
dc.creator.none.fl_str_mv Khawaldeh, Saed
Pervaiz, Usama
Rafiq, Azhar
Alkhawaldeh, Rami S.
author Khawaldeh, Saed
author_facet Khawaldeh, Saed
Pervaiz, Usama
Rafiq, Azhar
Alkhawaldeh, Rami S.
author_role author
author2 Pervaiz, Usama
Rafiq, Azhar
Alkhawaldeh, Rami S.
author2_role author
author
author
dc.subject.none.fl_str_mv Imatge -- Segmentació
Imaging segmentation
Cervell -- Imatgeria per ressonància magnètica
Brain -- Magnetic resonance imaging
Imatgeria mèdica
Imaging systems in medicine
Cervell -- Tumors
Brain -- Tumors
Glioblastoma multiforme
Glioblastoma multiforme
topic Imatge -- Segmentació
Imaging segmentation
Cervell -- Imatgeria per ressonància magnètica
Brain -- Magnetic resonance imaging
Imatgeria mèdica
Imaging systems in medicine
Cervell -- Tumors
Brain -- Tumors
Glioblastoma multiforme
Glioblastoma multiforme
description In recent years, Convolutional Neural Networks (ConvNets) have rapidly emerged as a widespread machine learning technique in a number of applications especially in the area of medical image classification and segmentation. In this paper, we propose a novel approach that uses ConvNet for classifying brain medical images into healthy and unhealthy brain images. The unhealthy images of brain tumors are categorized also into low grades and high grades. In particular, we use the modified version of the Alex Krizhevsky network (AlexNet) deep learning architecture on magnetic resonance images as a potential tumor classification technique. The classification is performed on the whole image where the labels in the training set are at the image level rather than the pixel level. The results showed a reasonable performance in characterizing the brain medical images with an accuracy of 91.16%
publishDate 2017
dc.date.none.fl_str_mv 2017
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
peer-reviewed
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/10256/18026
http://hdl.handle.net/10256/18026
url http://hdl.handle.net/10256/18026
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/doi/10.3390/app8010027
info:eu-repo/semantics/altIdentifier/eissn/2076-3417
dc.rights.none.fl_str_mv Attribution 4.0 International
http://creativecommons.org/licenses/by/4.0/
info:eu-repo/semantics/openAccess
rights_invalid_str_mv Attribution 4.0 International
http://creativecommons.org/licenses/by/4.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv MDPI (Multidisciplinary Digital Publishing Institute)
publisher.none.fl_str_mv MDPI (Multidisciplinary Digital Publishing Institute)
dc.source.none.fl_str_mv Applied Sciences, 2018, vol. 8, núm. 1, p. 27
Articles publicats (D-ATC)
reponame:Recercat. Dipósit de la Recerca de Catalunya
instname:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
instname_str Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
reponame_str Recercat. Dipósit de la Recerca de Catalunya
collection Recercat. Dipósit de la Recerca de Catalunya
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15,811543

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