Random Forest-Based Prediction of Bone Volume Fraction and Fracture Position from S-Parameters

Abstract:In this paper, we propose a method for predicting bone volume fraction (BVF) and fracture position by constructing a random forest model based on multichannel S-parameters. A nine-antenna microwave scanning system is designed and fabricated to acquire the multichannel S-parameter data. Bone-mimicking phantoms are developed, and corresponding experiments are conducted to validate the effectiveness of the proposed approach. Both synthetic and experimental results demonstrate the validity of the method.

Machine Learning (cs.LG)

arXiv:2607.23563 [cs.LG]

(or arXiv:2607.23563v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2607.23563 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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