Musculoskeletal images classification for detection of fractures using transfer learning

Detalhes bibliográficos
Autor(a) principal: Kandel, Ibrahem
Data de Publicação: 2020
Outros Autores: Castelli, Mauro, Popovič, Aleš
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10362/117843
Resumo: Kandel, I., Castelli, M., & Popovič, A. (2020). Musculoskeletal images classification for detection of fractures using transfer learning. Journal of Imaging, 6(11), 1-14. [127]. https://doi.org/10.3390/jimaging6110127
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spelling Musculoskeletal images classification for detection of fractures using transfer learningComputer visionConvolutional neural networksDeep learningImage classificationMedical imagesMusculoskeletal imagesTransfer learningRadiology Nuclear Medicine and imagingComputer Vision and Pattern RecognitionComputer Graphics and Computer-Aided DesignElectrical and Electronic EngineeringKandel, I., Castelli, M., & Popovič, A. (2020). Musculoskeletal images classification for detection of fractures using transfer learning. Journal of Imaging, 6(11), 1-14. [127]. https://doi.org/10.3390/jimaging6110127The classification of the musculoskeletal images can be very challenging, mostly when it is being done in the emergency room, where a decision must be made rapidly. The computer vision domain has gained increasing attention in recent years, due to its achievements in image classification. The convolutional neural network (CNN) is one of the latest computer vision algorithms that achieved state-of-the-art results. A CNN requires an enormous number of images to be adequately trained, and these are always scarce in the medical field. Transfer learning is a technique that is being used to train the CNN by using fewer images. In this paper, we study the appropriate method to classify musculoskeletal images by transfer learning and by training from scratch. We applied six state-of-the-art architectures and compared their performance with transfer learning and with a network trained from scratch. From our results, transfer learning did increase the model performance significantly, and, additionally, it made the model less prone to overfitting.Information Management Research Center (MagIC) - NOVA Information Management SchoolNOVA Information Management School (NOVA IMS)RUNKandel, IbrahemCastelli, MauroPopovič, Aleš2021-05-18T00:31:09Z2020-11-232020-11-23T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/article14application/pdfhttp://hdl.handle.net/10362/117843eng2313-433XPURE: 29767088https://doi.org/10.3390/jimaging6110127info: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:RCAAP2024-03-11T05:00:47Zoai:run.unl.pt:10362/117843Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T03:43:42.132206Repositório 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 Musculoskeletal images classification for detection of fractures using transfer learning
title Musculoskeletal images classification for detection of fractures using transfer learning
spellingShingle Musculoskeletal images classification for detection of fractures using transfer learning
Kandel, Ibrahem
Computer vision
Convolutional neural networks
Deep learning
Image classification
Medical images
Musculoskeletal images
Transfer learning
Radiology Nuclear Medicine and imaging
Computer Vision and Pattern Recognition
Computer Graphics and Computer-Aided Design
Electrical and Electronic Engineering
title_short Musculoskeletal images classification for detection of fractures using transfer learning
title_full Musculoskeletal images classification for detection of fractures using transfer learning
title_fullStr Musculoskeletal images classification for detection of fractures using transfer learning
title_full_unstemmed Musculoskeletal images classification for detection of fractures using transfer learning
title_sort Musculoskeletal images classification for detection of fractures using transfer learning
author Kandel, Ibrahem
author_facet Kandel, Ibrahem
Castelli, Mauro
Popovič, Aleš
author_role author
author2 Castelli, Mauro
Popovič, Aleš
author2_role author
author
dc.contributor.none.fl_str_mv Information Management Research Center (MagIC) - NOVA Information Management School
NOVA Information Management School (NOVA IMS)
RUN
dc.contributor.author.fl_str_mv Kandel, Ibrahem
Castelli, Mauro
Popovič, Aleš
dc.subject.por.fl_str_mv Computer vision
Convolutional neural networks
Deep learning
Image classification
Medical images
Musculoskeletal images
Transfer learning
Radiology Nuclear Medicine and imaging
Computer Vision and Pattern Recognition
Computer Graphics and Computer-Aided Design
Electrical and Electronic Engineering
topic Computer vision
Convolutional neural networks
Deep learning
Image classification
Medical images
Musculoskeletal images
Transfer learning
Radiology Nuclear Medicine and imaging
Computer Vision and Pattern Recognition
Computer Graphics and Computer-Aided Design
Electrical and Electronic Engineering
description Kandel, I., Castelli, M., & Popovič, A. (2020). Musculoskeletal images classification for detection of fractures using transfer learning. Journal of Imaging, 6(11), 1-14. [127]. https://doi.org/10.3390/jimaging6110127
publishDate 2020
dc.date.none.fl_str_mv 2020-11-23
2020-11-23T00:00:00Z
2021-05-18T00:31:09Z
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/10362/117843
url http://hdl.handle.net/10362/117843
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 2313-433X
PURE: 29767088
https://doi.org/10.3390/jimaging6110127
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv 14
application/pdf
dc.source.none.fl_str_mv reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron:RCAAP
instname_str Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron_str RCAAP
institution RCAAP
reponame_str Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
collection Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
repository.name.fl_str_mv Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
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