Automatic detection and localization of thighbone fractures in X-ray based on improved deep learning method

Deep learning is continuously promoting the development of fracture detection in medical images. In this study, we propose a novel two-stage region-based convolutional neural network for thighbone fractures detection. In this framework, the new network structure is designed to balance the information of each feature map in the feature pyramid of ResNeXt. In experiments, the pre-trained model is implemented on the dataset reported in the previous study, which includes 3842 thighbone X-ray radiographs. To compare the proposed framework with the latest detection techniques, transfer learning is employed to test all the state-of-the-art generic object detection algorithms on the same thighbone fracture dataset. Moreover, a few ablation experiments are given to demonstrate the effects of each component employed in the proposed framework and different hyperparameter settings on fracture detection. The experimental results show that the Average Precision of the proposed detection framework reaches 88.9% in thighbone fracture detection. This result proves the effectiveness of our framework and its superiority over other state-of-the-art methods. • A new deep learning framework is designed to detect thighbone fractures in X-rays. • The network structure includes a reconstructed feature pyramid and a attention block. • Our framework achieves an AP of 88.9% and outperforms all existing methods.