Detection of Bone Fractures Automatically with Enhanced Performance with Better Combination of Filtering and Neural Networks

Major parts affecting the accidents are bone and severe bone fractures lead to the death of human beings. Timely treatment is to be provided to the injuries, and sometimes orthopedic surgeons need to take immediate decisions regarding surgeries to the injuries. Among the available X-Ray, MRI, and CT scan options for bone fracture detection, X-Rays are mostly used due to its availability with less cost to all kinds of people. Here an automatic X-ray fractures detection technique is proposed which helps orthopaedecians to take an immediate decision for surgeries and to detect the fracture even without the support of an orthopaedician. In this paper, a back propagation neural network (BPNN) along with canny edge segmentation and a conservative smoothing filtering technique is proposed which helps in getting a better accuracy rate when compared to SVM (Support Vector Machine) and ANN (Artificial Neural Network). The proposed methodology also compares with the Harris corner detection technique. Among all other techniques a combination of BPNN with Canny edge detection and conservative smoothing proves to better performer for automatic bone fracture detection with accuracy of 91%.