부산대학교 도서관

Machine learning techniques are proven as a vital tool for the diagnosis and treatment of disease. The researchers are actively exploring new technology that may strengthen the state-of-the-practice in the clinical field. Automated bone fracture detection and classification have remained one of the popular research areas. However, traditionally developed techniques for the lower leg bone fracture method have faced the problem of detecting the existence of fractures and their location of occurrence. To overcome these problems, we proposed a transfer learning, Faster R-CNN deep learning model for fracture detection and classification with Region Proposal Network (RPN). Also, we retrained the top layer of the model by using inception v2 (version2) network architecture upon 50 x-ray images. This model was trained in 40k steps and its training stopped when loss remains only 0.0005. We evaluated the proposed model concerning detection and classification. We classify bone fracture x-ray images into two classes fracture and non-fracture also locate the location of fractures with a rectangle box. The overall accuracy has achieved from this method is 94% with respect to classification and detection. Our study shows that the proposed method is simple and efficient, which is worthwhile for dynamic detection, classification of fracture, now doctors and radiologists interact with more and more patient and overcome the workload. Furthermore, this approach improves the results, the run time performance and detection quality as compared to state-of-the-art techniques.