An attention‐based cascade R‐CNN model for sternum fracture detection in X‐ray images

Abstract

Fracture is one of the most common and unexpected traumas. If not treated in time, it may cause serious consequences such as joint stiffness, traumatic arthritis, and nerve injury. Using computer vision technology to detect fractures can reduce the workload and misdiagnosis of fractures and also improve the fracture detection speed. However, there are still some problems in sternum fracture detection, such as the low detection rate of small and occult fractures. In this work, the authors have constructed a dataset with 1227 labelled X-ray images for sternum fracture detection. The authors designed a fully automatic fracture detection model based on a deep convolution neural network (CNN). The authors used cascade R-CNN, attention mechanism, and atrous convolution to optimise the detection of small fractures in a large X-ray image with big local variations. The authors compared the detection results of YOLOv5 model, cascade R-CNN and other state-of-the-art models. The authors found that the convolution neural network based on cascade and attention mechanism models has a better detection effect and arrives at an mAP of 0.71, which is much better than using the YOLOv5 model (mAP = 0.44) and cascade R-CNN (mAP = 0.55).