Published Paper Details:

This proposed system is about automated techniques and methods to detect fracture. X-ray images are studied manually, but it is time-consuming and flat to errors. As X-ray images are more susceptible to noise, we used many preprocessing steps to remove noise and blur from the image. Thus, the system is able to spot fractures more accurately. The system detects fractures based on the type of fracture. The image is cleaned of noise and transformed into a clearer image so that the system can easily detect fracture. We used image processing methodologies to track bones. All unwanted objects, as well as smaller objects, are removed by the system. Finally, based on the connected component, the system detects a fracture. The system displays a bounding box around the fracture. Based on types of fracture images, preprocessing steps, and what a fracture detection system involves. The proposed system is able to detect bone dislocation with an 80% success rate, major bone fractures with 60-70% accuracy, and minor fractures with 50-60% accuracy.