Arm fracture detection in X rays based on improved deep convolutional neural network
Abstract In this paper, a novel deep learning method is proposed and applied to fracture detection in arm bone X rays. The main improvements include three aspects. First, a new backbone network is established based on feature pyramid architecture to gain more fractural information. Second, an image preprocessing procedure including opening operation and pixel value transformation is developed to enhance the contrast of original images. Third, the receptive field adjustment containing anchor scale reduction and tiny RoIs expansion is exploited to find more fractures. In the experiments, nearly 4000 arm fracture X ray radiographs collected from MURA dataset are annotated by experienced radiologists. The experiment results show that the proposed deep learning method achieves the state of the art AP in arm fracture detection and it has strong potential application in real clinical environments.
Comput. Electr. Eng.