Machine Learning Assisted Microwave Probing System for Comprehensive Human Tibial Fracture Detection

This paper presents a novel technique for diagnosing human bone fractures by integrating machine learning with microwave near-field MIMO probes. The proposed technique leverages a UWB MIMO microwave probe to differentiate between healthy and fractured bones. By estimating the differences in the reflection coefficient of the probe response for both normal and abnormal cases, the technique effectively distinguishes between healthy and non-healthy bones. To enhance the variation in the probe response, the S-parameters are taken with varying distances from the phantom model with values starting from 1mm. Several supervised machine learning algorithms are applied where the three best-performing algorithms are selected, and further analysis is conducted to determine the most effective algorithm among them. Experimental results demonstrate the effectiveness and accuracy of the proposed technique in diagnosing human bone fractures, showcasing its potential for practical application in the medical field. Further the study explores the development of machine learning models trained on microwave data to accurately identify and classify various fracture types, offering a non-invasive and potentially more accessible method for early detection.