Severity classification in cases of Collagen VI-related myopathy with Convolutional Neural Networks and handcrafted texture features
| Autor(a) principal: | |
|---|---|
| Data de Publicação: | 2022 |
| Outros Autores: | , , |
| Tipo de documento: | Artigo |
| Idioma: | eng |
| Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
| Texto Completo: | http://hdl.handle.net/10400.6/12272 |
Resumo: | (C) 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. |
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Severity classification in cases of Collagen VI-related myopathy with Convolutional Neural Networks and handcrafted texture featuresCollagen VI-related myopathyMRIComputer-aided diagnosisTexture analysisConvolutional Neural Networks(C) 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.Magnetic Resonance Imaging (MRI) is a non-invasive tool for the clinical assessment of low-prevalence neuromuscular disorders. Automated diagnosis methods might reduce the need for biopsies and provide valuable information on disease follow-up. In this paper, three methods are proposed to classify target muscles in Collagen VI-related myopathy cases, based on their degree of involvement, notably a Convolutional Neural Network, a Fully Connected Network to classify texture features, and a hybrid method combining the two feature sets. The proposed methods were evaluated on axial T1-weighted Turbo Spin-Echo MRI from 26 subjects, including Ullrich Congenital Muscular Dystrophy and Bethlem Myopathy patients at different evolution stages. The hybrid model achieved the best cross-validation results, with a global accuracy of 93.8%, and F-scores of 0.99, 0.82, and 0.95, for healthy, mild and moderate/severe cases, respectively.IEEEuBibliorumRodrigues, RafaelQuijano-Roy, SusanaCarlier, Robert-YvesPinheiro, Antonio M. G.2022-07-11T08:48:04Z2022-102022-10-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10400.6/12272enginfo:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2023-12-15T09:55:22Zoai:ubibliorum.ubi.pt:10400.6/12272Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T00:51:55.174708Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse |
| dc.title.none.fl_str_mv |
Severity classification in cases of Collagen VI-related myopathy with Convolutional Neural Networks and handcrafted texture features |
| title |
Severity classification in cases of Collagen VI-related myopathy with Convolutional Neural Networks and handcrafted texture features |
| spellingShingle |
Severity classification in cases of Collagen VI-related myopathy with Convolutional Neural Networks and handcrafted texture features Rodrigues, Rafael Collagen VI-related myopathy MRI Computer-aided diagnosis Texture analysis Convolutional Neural Networks |
| title_short |
Severity classification in cases of Collagen VI-related myopathy with Convolutional Neural Networks and handcrafted texture features |
| title_full |
Severity classification in cases of Collagen VI-related myopathy with Convolutional Neural Networks and handcrafted texture features |
| title_fullStr |
Severity classification in cases of Collagen VI-related myopathy with Convolutional Neural Networks and handcrafted texture features |
| title_full_unstemmed |
Severity classification in cases of Collagen VI-related myopathy with Convolutional Neural Networks and handcrafted texture features |
| title_sort |
Severity classification in cases of Collagen VI-related myopathy with Convolutional Neural Networks and handcrafted texture features |
| author |
Rodrigues, Rafael |
| author_facet |
Rodrigues, Rafael Quijano-Roy, Susana Carlier, Robert-Yves Pinheiro, Antonio M. G. |
| author_role |
author |
| author2 |
Quijano-Roy, Susana Carlier, Robert-Yves Pinheiro, Antonio M. G. |
| author2_role |
author author author |
| dc.contributor.none.fl_str_mv |
uBibliorum |
| dc.contributor.author.fl_str_mv |
Rodrigues, Rafael Quijano-Roy, Susana Carlier, Robert-Yves Pinheiro, Antonio M. G. |
| dc.subject.por.fl_str_mv |
Collagen VI-related myopathy MRI Computer-aided diagnosis Texture analysis Convolutional Neural Networks |
| topic |
Collagen VI-related myopathy MRI Computer-aided diagnosis Texture analysis Convolutional Neural Networks |
| description |
(C) 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. |
| publishDate |
2022 |
| dc.date.none.fl_str_mv |
2022-07-11T08:48:04Z 2022-10 2022-10-01T00:00:00Z |
| dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
| dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
| format |
article |
| status_str |
publishedVersion |
| dc.identifier.uri.fl_str_mv |
http://hdl.handle.net/10400.6/12272 |
| url |
http://hdl.handle.net/10400.6/12272 |
| dc.language.iso.fl_str_mv |
eng |
| language |
eng |
| dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
| eu_rights_str_mv |
openAccess |
| dc.format.none.fl_str_mv |
application/pdf |
| dc.publisher.none.fl_str_mv |
IEEE |
| publisher.none.fl_str_mv |
IEEE |
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reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação instacron:RCAAP |
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Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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RCAAP |
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RCAAP |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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