ANALYSIS OF TIBIA-FIBULA BONE FRACTURE USING DEEP LEARNING TECHNIQUE FROM X-RAY IMAGES

Abstract

Deep learning technologies have become a leading tool for disease diagnosis supporting and timely treatment. Many approaches have been developed for the detection and classification of fractures in human bones. These approaches vary from several parameters producing different detection and classification traits. Therefore, fracture detection, along with recognizing its category, is helpful for radiologists and doctors to analyze and handle fracture cases effectively. In this study, we employed a Faster R-CNN transfer-learning technique. The model was retrained using 50 X-ray images of tibia-fibula bone fractures. For the evaluation of this study, we used parameters such as Kappa coefficient and the mean average precision. The overall accuracy of this proposed method has been 97%. It is comprehensively inspected and correlated with the earlier work on bone fractures, concerning training, detection, classification, and efficiency. The proposed work proved to have a good impact on accurate classification and detection of fractures. Moreover, here we analyzed six bone fracture classes: transverse, spiral, oblique, linear, comminuted, and normal. The best configuration tends to show that this study is tremendously correct and economical with high accuracy. This work shows that the proposed approach is an effective and useful technique for the dynamic detection, classification, and analysis of various types of fractures. Furthermore, this approach improved the results, the run time performance, and detection quality compared with the state-of-the-art techniques used in this area.

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