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dc.contributor.author
Gonzalez, Mailen
dc.contributor.author
Fuertes García, Jose M.
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Lucena López, Manuel J.
dc.contributor.author
Abdala, Ruben
dc.contributor.author
Massa, José María
dc.date.available
2024-09-17T11:01:29Z
dc.date.issued
2024-06
dc.identifier.citation
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
dc.identifier.issn
2158-107X
dc.identifier.uri
http://hdl.handle.net/11336/244402
dc.description.abstract
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.
dc.format
application/pdf
dc.language.iso
eng
dc.publisher
The Science and Information Organization
dc.rights
info:eu-repo/semantics/openAccess
dc.rights.uri
https://creativecommons.org/licenses/by/2.5/ar/
dc.subject
OSTEOPOROSIS
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DUAL X RAY ABSORPTIOMETRY
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TRABECULAR BONE SCORE
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CONVOLUTIONAL NEURAL NETWORK
dc.subject.classification
Ciencias de la Información y Bioinformática
dc.subject.classification
Ciencias de la Computación e Información
dc.subject.classification
CIENCIAS NATURALES Y EXACTAS
dc.title
Bone Quality Classification of Dual Energy X-ray Absorptiometry Images Using Convolutional Neural Network Models
dc.type
info:eu-repo/semantics/article
dc.type
info:ar-repo/semantics/artículo
dc.type
info:eu-repo/semantics/publishedVersion
dc.date.updated
2024-08-26T14:56:24Z
dc.identifier.eissn
2156-5570
dc.journal.volume
15
dc.journal.number
6
dc.journal.pagination
554-1560
dc.journal.pais
Reino Unido
dc.description.fil
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
dc.description.fil
Fil: Fuertes García, Jose M.. Universidad de Jaén; España
dc.description.fil
Fil: Lucena López, Manuel J.. Universidad de Jaén; España
dc.description.fil
Fil: Abdala, Ruben. Instituto de Diagnostico E Investigaciones Metabolicas (idim);
dc.description.fil
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
dc.journal.title
International Journal of Advanced Computer Science and Applications
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/url/http://thesai.org/Publications/ViewPaper?Volume=15&Issue=6&Code=ijacsa&SerialNo=154
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/doi/http://dx.doi.org/10.14569/IJACSA.2024.01506154
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