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

The assessment of bone trabecular quality degradation 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 artifi...

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Detalles Bibliográficos
Autores: González, Mailen, Fuertes, José M., Lucena López, Manuel, Abdala, Rubén, Massa, José M.
Tipo de recurso: artículo
Estado:Versión aceptada para publicación
Fecha de publicación:2024
País:España
Institución:Universidad de Jaén
Repositorio:RUJA. Repositorio Institucional de la Producción Científica de la Universidad de Jaén
OAI Identifier:oai:ruja.ujaen.es:10953/3938
Acceso en línea:https://dx.doi.org/10.14569/IJACSA.2024.01506154
https://thesai.org/Publications/ViewPaper?Volume=15&Issue=6&Code=ijacsa&SerialNo=154
https://hdl.handle.net/10953/3938
Access Level:acceso abierto
Palabra clave:Osteoporosis
Dual Energy X-ray Absorptiometry (DXA)
Trabecular Bone Score (TBS)
Classification
Convolutional Neural Network (CNN)
681.3
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spelling Bone Quality Classification of Dual Energy X-ray Absorptiometry Images using Convolutional Neural Network ModelsGonzález, MailenFuertes, José M.Lucena López, ManuelAbdala, RubénMassa, José M.OsteoporosisDual Energy X-ray Absorptiometry (DXA)Trabecular Bone Score (TBS)ClassificationConvolutional Neural Network (CNN)681.3The assessment of bone trabecular quality degradation 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.Fellowship of Consejo Nacional de Investigaciones Cientı́ficas y Técnicas (CONICET) and Escuela Doctoral de la Universidad de Jaén (EDUJA).Science and Information Organization202520252024info:eu-repo/semantics/articleinfo:eu-repo/semantics/acceptedVersionapplication/pdfhttps://dx.doi.org/10.14569/IJACSA.2024.01506154https://thesai.org/Publications/ViewPaper?Volume=15&Issue=6&Code=ijacsa&SerialNo=154https://hdl.handle.net/10953/3938reponame:RUJA. Repositorio Institucional de la Producción Científica de la Universidad de Jaéninstname:Universidad de JaénInglésInternational Journal of Advanced Computer Science and ApplicationsAtribución-NoComercial-SinDerivadas 3.0 Españahttp://creativecommons.org/licenses/by-nc-nd/3.0/es/info:eu-repo/semantics/openAccessoai:ruja.ujaen.es:10953/39382026-06-24T12:41:07Z
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
González, Mailen
Osteoporosis
Dual Energy X-ray Absorptiometry (DXA)
Trabecular Bone Score (TBS)
Classification
Convolutional Neural Network (CNN)
681.3
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 González, Mailen
Fuertes, José M.
Lucena López, Manuel
Abdala, Rubén
Massa, José M.
author González, Mailen
author_facet González, Mailen
Fuertes, José M.
Lucena López, Manuel
Abdala, Rubén
Massa, José M.
author_role author
author2 Fuertes, José M.
Lucena López, Manuel
Abdala, Rubén
Massa, José M.
author2_role author
author
author
author
dc.subject.none.fl_str_mv Osteoporosis
Dual Energy X-ray Absorptiometry (DXA)
Trabecular Bone Score (TBS)
Classification
Convolutional Neural Network (CNN)
681.3
topic Osteoporosis
Dual Energy X-ray Absorptiometry (DXA)
Trabecular Bone Score (TBS)
Classification
Convolutional Neural Network (CNN)
681.3
description The assessment of bone trabecular quality degradation 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
2025
2025
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/acceptedVersion
format article
status_str acceptedVersion
dc.identifier.none.fl_str_mv https://dx.doi.org/10.14569/IJACSA.2024.01506154
https://thesai.org/Publications/ViewPaper?Volume=15&Issue=6&Code=ijacsa&SerialNo=154
https://hdl.handle.net/10953/3938
url https://dx.doi.org/10.14569/IJACSA.2024.01506154
https://thesai.org/Publications/ViewPaper?Volume=15&Issue=6&Code=ijacsa&SerialNo=154
https://hdl.handle.net/10953/3938
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv International Journal of Advanced Computer Science and Applications
dc.rights.none.fl_str_mv Atribución-NoComercial-SinDerivadas 3.0 España
http://creativecommons.org/licenses/by-nc-nd/3.0/es/
info:eu-repo/semantics/openAccess
rights_invalid_str_mv Atribución-NoComercial-SinDerivadas 3.0 España
http://creativecommons.org/licenses/by-nc-nd/3.0/es/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Science and Information Organization
publisher.none.fl_str_mv Science and Information Organization
dc.source.none.fl_str_mv reponame:RUJA. Repositorio Institucional de la Producción Científica de la Universidad de Jaén
instname:Universidad de Jaén
instname_str Universidad de Jaén
reponame_str RUJA. Repositorio Institucional de la Producción Científica de la Universidad de Jaén
collection RUJA. Repositorio Institucional de la Producción Científica de la Universidad de Jaén
repository.name.fl_str_mv
repository.mail.fl_str_mv
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