A Systematic approach to improve Support Vector Machine applied to ultrasonic guided wave spectrum image classification

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dc.contributor.authorMiranda, Diego
dc.contributor.authorOlivares, Rodrigo
dc.contributor.authorMuñoz, Roberto
dc.contributor.authorJean-Gabriel Minonzio
dc.date.accessioned2022-11-30T02:46:36Z
dc.date.available2022-11-30T02:46:36Z
dc.date.issued2021
dc.description.abstractOsteoporosis is a skeletal disorder characterized by low bone mass, which compromises its resistance and increases the risk of fractures, and is a widespread problem worldwide. Currently, the gold standard for assessing fracture risk is the measurement of the areal bone mineral density with Dual-Energy X-ray Absorptiometry. Several ultrasound techniques have been presented as alternatives. It has been shown that the estimation of cortical thickness and porosity, obtained by Bi-Directional Axial Transmission, are associated with non-traumatic fractures in postmenopausal women. Cortical parameters were derived from the comparison between experimental and theoretical guided modes. However, this model-based inverse approach tends to fail for the patients associated with poor guided mode information. A recent study has shown the potential of an automatic classification tool, Support Vector Machine, to analyze guided wave spectrum images independently of any waveguide model. The aim of this study is to explore how the classification accuracy varies with the number of features. Optimization was done using the Particle Swarm Optimization algorithm, while adjustment was made considering age, body mass index, and cortisone intake. The results show that adjusting the data and optimizing the parameters improved classification. Moreover, the number of features was reduced from 32 to 15, with 73.5% accuracy comparable to the gold standard.en_ES
dc.facultadFacultad de Ingenieríaen_ES
dc.file.nameMiranda_Sys2021.pdf
dc.identifier.citationD. Miranda, R. Olivares, R. Munoz and J. -G. Minonzio, "A Systematic approach to improve Support Vector Machine applied to ultrasonic guided wave spectrum image classification," 2021 IEEE International Ultrasonics Symposium (IUS), 2021, pp. 1-3, doi: 10.1109/IUS52206.2021.9593693.en_ES
dc.identifier.doi10.1109/IUS52206.2021.9593693.
dc.identifier.urihttp://repositoriobibliotecas.uv.cl/handle/uvscl/7435
dc.languageen
dc.publisherIEEE
dc.rightsThis is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com
dc.source2021 IEEE International Ultrasonics Symposium (IUS)
dc.subjectOSTEOPOROSISen_ES
dc.subjectULTRASONIC IMAGINGen_ES
dc.subjectULTRASONIC VARIABLES MEASUREMENTen_ES
dc.subjectSUPPORT VECTOR MACHINE CLASSIFICATIONen_ES
dc.subjectTOOLSen_ES
dc.subjectACOUSTICSen_ES
dc.subjectCLASSIFICATION ALGORITHMSen_ES
dc.titleA Systematic approach to improve Support Vector Machine applied to ultrasonic guided wave spectrum image classification
dc.typeArticulo
uv.departamentoEscuela de Ingenieria Informatica
uv.notageneralNo disponible para descarga

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