Bone Quality Classification of Dual Energy X-ray Absorptiometry Images Using Convolutional Neural Network Models

Autores
Gonzalez, Mailen; Fuertes García, Jose M.; Lucena López, Manuel J.; Abdala, Ruben; Massa, José María
Año de publicación
2024
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
The assessment of bone trabecular quality degrada-tion is important for the detection of diseases such as osteoporosis. The gold standard for its diagnosis is the Dual Energy X-ray Absorptiometry (DXA) image modality. The analysis of these images is a topic of growing interest, especially with artificial intelligence techniques. This work proposes the detection of a degraded bone structure from DXA images using some approaches based on the learning of Trabecular Bone Score (TBS) ranges. The proposed models are supported by intelligent systems based on convolutional neural networks using two kinds of approaches: ad hoc architectures and knowledge transfer systems in deep network architectures, such as AlexNet, ResNet, VGG, SqueezeNet, and DenseNet retrained with DXA images. For both approaches, experimental studies were made comparing the proposed models in terms of effectiveness and training time, achieving an F1-Score result of approximately 0.75 to classify the bone structure as degraded or normal according to its TBS range.
Fil: Gonzalez, Mailen. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil; Argentina. Universidad Nacional del Centro de la Provincia de Buenos Aires. Facultad de Ciencias Exactas. Instituto de Investigaciones en Tecnología Informática Avanzada; Argentina
Fil: Fuertes García, Jose M.. Universidad de Jaén; España
Fil: Lucena López, Manuel J.. Universidad de Jaén; España
Fil: Abdala, Ruben. Instituto de Diagnostico E Investigaciones Metabolicas (idim);
Fil: Massa, José María. Universidad Nacional del Centro de la Provincia de Buenos Aires. Facultad de Ciencias Exactas. Instituto de Investigaciones en Tecnología Informática Avanzada; Argentina
Materia
OSTEOPOROSIS
DUAL X RAY ABSORPTIOMETRY
TRABECULAR BONE SCORE
CONVOLUTIONAL NEURAL NETWORK
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/244402
Acceder
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network_name_str CONICET Digital (CONICET)
spelling Bone Quality Classification of Dual Energy X-ray Absorptiometry Images Using Convolutional Neural Network ModelsGonzalez, MailenFuertes García, Jose M.Lucena López, Manuel J.Abdala, RubenMassa, José MaríaOSTEOPOROSISDUAL X RAY ABSORPTIOMETRYTRABECULAR BONE SCORECONVOLUTIONAL NEURAL NETWORKhttps://purl.org/becyt/ford/1.2https://purl.org/becyt/ford/1The assessment of bone trabecular quality degrada-tion is important for the detection of diseases such as osteoporosis. The gold standard for its diagnosis is the Dual Energy X-ray Absorptiometry (DXA) image modality. The analysis of these images is a topic of growing interest, especially with artificial intelligence techniques. This work proposes the detection of a degraded bone structure from DXA images using some approaches based on the learning of Trabecular Bone Score (TBS) ranges. The proposed models are supported by intelligent systems based on convolutional neural networks using two kinds of approaches: ad hoc architectures and knowledge transfer systems in deep network architectures, such as AlexNet, ResNet, VGG, SqueezeNet, and DenseNet retrained with DXA images. For both approaches, experimental studies were made comparing the proposed models in terms of effectiveness and training time, achieving an F1-Score result of approximately 0.75 to classify the bone structure as degraded or normal according to its TBS range.Fil: Gonzalez, Mailen. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil; Argentina. Universidad Nacional del Centro de la Provincia de Buenos Aires. Facultad de Ciencias Exactas. Instituto de Investigaciones en Tecnología Informática Avanzada; ArgentinaFil: Fuertes García, Jose M.. Universidad de Jaén; EspañaFil: Lucena López, Manuel J.. Universidad de Jaén; EspañaFil: Abdala, Ruben. Instituto de Diagnostico E Investigaciones Metabolicas (idim);Fil: Massa, José María. Universidad Nacional del Centro de la Provincia de Buenos Aires. Facultad de Ciencias Exactas. Instituto de Investigaciones en Tecnología Informática Avanzada; ArgentinaThe Science and Information Organization2024-06info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/244402Gonzalez, Mailen; Fuertes García, Jose M.; Lucena López, Manuel J.; Abdala, Ruben; Massa, José María; Bone Quality Classification of Dual Energy X-ray Absorptiometry Images Using Convolutional Neural Network Models; The Science and Information Organization; International Journal of Advanced Computer Science and Applications; 15; 6; 6-2024; 554-15602158-107X2156-5570CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/http://thesai.org/Publications/ViewPaper?Volume=15&Issue=6&Code=ijacsa&SerialNo=154info:eu-repo/semantics/altIdentifier/doi/10.14569/IJACSA.2024.01506154info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-03T09:52:48Zoai:ri.conicet.gov.ar:11336/244402instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-09-03 09:52:48.548CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Bone Quality Classification of Dual Energy X-ray Absorptiometry Images Using Convolutional Neural Network Models
title Bone Quality Classification of Dual Energy X-ray Absorptiometry Images Using Convolutional Neural Network Models
spellingShingle Bone Quality Classification of Dual Energy X-ray Absorptiometry Images Using Convolutional Neural Network Models
Gonzalez, Mailen
OSTEOPOROSIS
DUAL X RAY ABSORPTIOMETRY
TRABECULAR BONE SCORE
CONVOLUTIONAL NEURAL NETWORK
title_short Bone Quality Classification of Dual Energy X-ray Absorptiometry Images Using Convolutional Neural Network Models
title_full Bone Quality Classification of Dual Energy X-ray Absorptiometry Images Using Convolutional Neural Network Models
title_fullStr Bone Quality Classification of Dual Energy X-ray Absorptiometry Images Using Convolutional Neural Network Models
title_full_unstemmed Bone Quality Classification of Dual Energy X-ray Absorptiometry Images Using Convolutional Neural Network Models
title_sort Bone Quality Classification of Dual Energy X-ray Absorptiometry Images Using Convolutional Neural Network Models
dc.creator.none.fl_str_mv Gonzalez, Mailen
Fuertes García, Jose M.
Lucena López, Manuel J.
Abdala, Ruben
Massa, José María
author Gonzalez, Mailen
author_facet Gonzalez, Mailen
Fuertes García, Jose M.
Lucena López, Manuel J.
Abdala, Ruben
Massa, José María
author_role author
author2 Fuertes García, Jose M.
Lucena López, Manuel J.
Abdala, Ruben
Massa, José María
author2_role author
author
author
author
dc.subject.none.fl_str_mv OSTEOPOROSIS
DUAL X RAY ABSORPTIOMETRY
TRABECULAR BONE SCORE
CONVOLUTIONAL NEURAL NETWORK
topic OSTEOPOROSIS
DUAL X RAY ABSORPTIOMETRY
TRABECULAR BONE SCORE
CONVOLUTIONAL NEURAL NETWORK
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.2
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv The assessment of bone trabecular quality degrada-tion is important for the detection of diseases such as osteoporosis. The gold standard for its diagnosis is the Dual Energy X-ray Absorptiometry (DXA) image modality. The analysis of these images is a topic of growing interest, especially with artificial intelligence techniques. This work proposes the detection of a degraded bone structure from DXA images using some approaches based on the learning of Trabecular Bone Score (TBS) ranges. The proposed models are supported by intelligent systems based on convolutional neural networks using two kinds of approaches: ad hoc architectures and knowledge transfer systems in deep network architectures, such as AlexNet, ResNet, VGG, SqueezeNet, and DenseNet retrained with DXA images. For both approaches, experimental studies were made comparing the proposed models in terms of effectiveness and training time, achieving an F1-Score result of approximately 0.75 to classify the bone structure as degraded or normal according to its TBS range.
Fil: Gonzalez, Mailen. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil; Argentina. Universidad Nacional del Centro de la Provincia de Buenos Aires. Facultad de Ciencias Exactas. Instituto de Investigaciones en Tecnología Informática Avanzada; Argentina
Fil: Fuertes García, Jose M.. Universidad de Jaén; España
Fil: Lucena López, Manuel J.. Universidad de Jaén; España
Fil: Abdala, Ruben. Instituto de Diagnostico E Investigaciones Metabolicas (idim);
Fil: Massa, José María. Universidad Nacional del Centro de la Provincia de Buenos Aires. Facultad de Ciencias Exactas. Instituto de Investigaciones en Tecnología Informática Avanzada; Argentina
description The assessment of bone trabecular quality degrada-tion is important for the detection of diseases such as osteoporosis. The gold standard for its diagnosis is the Dual Energy X-ray Absorptiometry (DXA) image modality. The analysis of these images is a topic of growing interest, especially with artificial intelligence techniques. This work proposes the detection of a degraded bone structure from DXA images using some approaches based on the learning of Trabecular Bone Score (TBS) ranges. The proposed models are supported by intelligent systems based on convolutional neural networks using two kinds of approaches: ad hoc architectures and knowledge transfer systems in deep network architectures, such as AlexNet, ResNet, VGG, SqueezeNet, and DenseNet retrained with DXA images. For both approaches, experimental studies were made comparing the proposed models in terms of effectiveness and training time, achieving an F1-Score result of approximately 0.75 to classify the bone structure as degraded or normal according to its TBS range.
publishDate 2024
dc.date.none.fl_str_mv 2024-06
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/11336/244402
Gonzalez, Mailen; Fuertes García, Jose M.; Lucena López, Manuel J.; Abdala, Ruben; Massa, José María; Bone Quality Classification of Dual Energy X-ray Absorptiometry Images Using Convolutional Neural Network Models; The Science and Information Organization; International Journal of Advanced Computer Science and Applications; 15; 6; 6-2024; 554-1560
2158-107X
2156-5570
CONICET Digital
CONICET
url http://hdl.handle.net/11336/244402
identifier_str_mv Gonzalez, Mailen; Fuertes García, Jose M.; Lucena López, Manuel J.; Abdala, Ruben; Massa, José María; Bone Quality Classification of Dual Energy X-ray Absorptiometry Images Using Convolutional Neural Network Models; The Science and Information Organization; International Journal of Advanced Computer Science and Applications; 15; 6; 6-2024; 554-1560
2158-107X
2156-5570
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/http://thesai.org/Publications/ViewPaper?Volume=15&Issue=6&Code=ijacsa&SerialNo=154
info:eu-repo/semantics/altIdentifier/doi/10.14569/IJACSA.2024.01506154
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
application/pdf
dc.publisher.none.fl_str_mv The Science and Information Organization
publisher.none.fl_str_mv The Science and Information Organization
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
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score 13.13397

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