Musculoskeletal images classification for detection of fractures using transfer learning
Autor(a) principal: | |
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Data de Publicação: | 2020 |
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/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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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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7160 |
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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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1799138045965893632 |