AN APPROACH FOR HAND BONE FRACTURE DETECTION IN X-RAY IMAGES WITH DEEP LEARNING TECHNIQUES

Abstract

Hand fractures are easy to occur in everyday life, especially for those who often participate in sporting and working activities. Hand fractures are not too dangerous, but they directly affect daily life, causing many inconveniences. If not treated promptly or misdiagnosed, it will cause a loss of aesthetics, affecting the function of grasping and tactile ability to recognize objects later. With a diagnosis of a hand fracture, medical practitioners often order an X-ray because they can see details about the fracture line, fracture pattern, and soft tissue damage. Based on that, doctors will use appropriate treatment methods. This study examined the performance of diagnosing bone fractures, detect the location and shape of cracks based on two methods: instance segmentation and semantic segmentation. The results obtained in diagnosing fractures are 96,73%.