Research on Classification of Wrist Fracture Based on Faster R-CNN Image Retrieval
YANG Feng1, CHEN Lei1, XING Mengmeng2
1. Department of Asset Equipment, Affiliated Hospital of Shandong University of Traditional Chinese Medicine, Jinan Shandong 250013,
China; 2. Equipment Division, Department of Medical Engineering, China Rehabilitation Research Center, Beijing 100071, China
Abstract：Objective In order to overcome the complicated arrangement of focal areas in X-ray images of wrist, which is easy to
cause missed diagnosis, misdiagnosis and low diagnosis efficiency by orthopedic doctors, to propose a wrist classification algorithm
for medical image retrieval bases on faster region-convolutional neural network (Faster R-CNN). Methods Firstly, the limited
contrast adaptive histogram equalization algorithm was used to preprocess the wrist sample data, and then Faster R-CNN was used
to locate the region of interest in the wrist image and extract its directional gradient histogram features, Haralick texture features
and depth features. After using convolutional neural network to effectively fuse various features extracted, they were fed into the
improved image retrieval diagnosis model in this paper to complete the classification task of wrist images. Results The average
area under curve of the wrist image detection model proposed in this paper was 0.893, and the diagnostic accuracy was better than
the results of the comparative experiment, which was about 5% higher than the previous research methods. Conclusion This paper
proves that the proposed Faster R-CNN image retrieval for wrist fracture classification is effective and robust.
杨 锋1，陈 雷1，邢蒙蒙2. 基于Faster R-CNN的图像检索手腕骨折分类研究[J]. 中国医疗设备, 2023, 38(2): 1-6.
YANG Feng1, CHEN Lei1, XING Mengmeng2. Research on Classification of Wrist Fracture Based on Faster R-CNN Image Retrieval. China Medical Devices, 2023, 38(2): 1-6.
