Design of a Fracture Detection System based on Deep Program in a Convolutional Neural Network

Abstract

This paper presents an efficient method for finding fractures in bones. For this purpose, the pre-processing set includes increasing the quality of images, removing additional objects, removing noise and rotating images. The input images then enter the machine learning phase to detect the final fracture. At this stage, a Convolutional Neural Networks is created by Genetic Programming (GP). In this way, learning models are implemented in the form of GP programs. And evolve during the evolution of this program. Then finally the best program for classifying incoming images is selected. The data set in this work is divided into training and test friends who have nothing in common. The ratio of training data to test is equal to 80 to 20. Finally, experimental results show good results for the proposed method for bone fractures.