Miranda, DiegoOlivares, RodrigoMuñoz, RobertoJean-Gabriel Minonzio2022-11-302022-11-302021D. 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.http://repositoriobibliotecas.uv.cl/handle/uvscl/7435Osteoporosis 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.This 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.comOSTEOPOROSISULTRASONIC IMAGINGULTRASONIC VARIABLES MEASUREMENTSUPPORT VECTOR MACHINE CLASSIFICATIONTOOLSACOUSTICSCLASSIFICATION ALGORITHMSArticulo10.1109/IUS52206.2021.9593693.