Microwave Diagnosis of Bone Fractures: An Artificial Intelligence-Based Approach
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A4 Artikkeli konferenssijulkaisussa
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Date
2023
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Language
en
Pages
4
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2023 53rd European Microwave Conference, EuMC 2023, pp. 440-443
Abstract
This paper evaluates the suitability of a Deep Neural Network (DNN) for diagnosing bone fractures through non-invasive radio frequency wave propagation. The DNN is trained using S-parameter profiles instead of X-ray images to avoid labeling and data collection challenges. The resulting network can classify diverse fracture types (normal, transverse, oblique, and comminuted) and simultaneously determine the size of cracks. Using a portable device, the proposed system provides fast preliminary diagnoses in emergency settings where radiologists are unavailable. The DNN was trained using human body models with varying tissue diameters to simulate different anatomical regions. Numerical results demonstrate successful training without overfitting, and experiments on sheep femur bones in liquid phantom validate the accuracy of fracture classification without harmful X-rays.Description
Publisher Copyright: © 2023 European Microwave Association (EuMA).
Keywords
Bone fracture diagnosis, deep learning, phantom measurement system, scattering parameter
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Citation
Ghorbani, F, Beyraghi, S, Shabanpour, J, Lajevardi, M E, Nayyeri, V, Chen, P Y & Ramahi, O M 2023, Microwave Diagnosis of Bone Fractures: An Artificial Intelligence-Based Approach . in 2023 53rd European Microwave Conference, EuMC 2023 . IEEE, pp. 440-443, European Microwave Conference, Berlin, Germany, 19/09/2023 . https://doi.org/10.23919/EuMC58039.2023.10290196