Detection and localization of hand fractures based on GA_Faster R-CNN

Abstract

X-ray imaging is the primary diagnostic tool for clinical diagnosis of suspected fracture. Hand fracture (HF) is a world-leading health problem for children, adolescents and the elderly. A missed diagnosis of hand fracture on radiography may lead to severe consequences for patients, resulting in delayed treatment and poor recovery of function. Nevertheless, many hand fractures are fairly insidious, which often lead to misdiagnosis. In this dissertation, we propose GA_Faster R-CNN in which a guided anchoring method (GA) of GA_RPN is applied to detect and localize hand fractures in radiographs. Our new guided anchoring method makes the anchor generation more accurate and efficient, greatly improves the network performance, and saves computing power. In our work, Feature Pyramid Network (FPN) is used to solve the problem of tiny object detection which mostly appears at the joint of fingertips and knuckles. In addition, Balanced L1 Loss is applied to adapt to the imbalance of learning tasks. We evaluate the proposed algorithm on a HF dataset containing 3,067 X-ray radiographs, 2,453 of which are assigned as the training dataset and 614 as the testing dataset. The present framework achieved accuracies of 97%–99% and an average precision (AP) of 70.7%, thereby outperforming the previous state-of-the-art methods for detecting HF. As a consequence, the GA_Faster R-CNN has great potential for clinical applications.

Keywords

• hand fracture (HF)
• Computer-aided diagnosis (CAD)
• Convolutional neural network (CNN)
• Guided anchoring
• Feature pyramid network (FPN)