Computer-Assisted Bone Fractures Detection Based on Depth Feature

Abstract

Nowadays, computer aided diagnosis (CAD) system become popular because it improves the diagnostic efficiency of the disease compared to the early diagnosis of the various diseases for the doctors and the medical expert specialists. Similarly, bone fractures is a common problem due to pressure, accident and osteoporosis. Bone fractures detection using computer vision is getting more and more important in CAD system because it can help to reduce workload of the doctor by screening out the easy case. The purpose of this work was to detect whether fractures occurred in seven different parts of the human body in the MURA database. The process of a bone fractures detection is mainly divided into three steps, which includes preprocessing, feature extraction and classification. In preprocessing, the 40561 sizes of images in the MURA data set were reshaped to same size and the grayscale images were changed to the RGB images. The convolutional neural network (CNN) was then used to extract the depth features of the bone images. Finally, three classic classifiers were selected, such as support vector machine (SVM), extreme learning machine (ELM) and random forest (RF) to detect bone cracks or non-cracks. Then compare it with the Alexnet model and the classification results of the Wu Enda team. The result shows that the classification of SVM based depth features is the best and the accuracy can reach 78.63%. The experimental result indicates that the depth features of the extracted bone images provide a new feature representation method for the prediction of bone fracturess, which greatly improves the accuracy of clinical diagnosis of bone fracturess.