Skull Fracture Detection Method Based on Improved Feature Pyramid Network

Skull fracture detection is to detect and locate the area in the skull images. Because the skull fracture areas are relatively tiny, it is generally difficult to detect. Moreover, the fracture areas are easy to be confused with normal sutures. Hence, skull fracture detection is a small object detection. For small object detection in the medical images, the object detection methods based on deep learning is difficult to achieve satisfactory results. In order to further improve the accuracy of skull fracture detection, this paper proposes a novel skull fracture detection method based on Feature Pyramid Network (FPN) with the attention mechanism. This method uses the criss-cross attention mechanism to highlight the contextual information of the objects in each layer of the feature maps in the feature pyramid, which effectively enhances the target information in the feature pyramid. Because data augmentation can effectively improves the detection accuracy, this paper proposes a new data augmentation strategy to improve the detection robustness of the network. In addition, in order to more accurately reflect the locate effects of candidate boxes, this paper proposes a new Intersection over Union (IoU) calculation method, which is based on the distance between the center points of the predicted boxes and the labeled boxes, adding a scale factor to the original calculation method of IoU. The new method can better reflect the actual situation. We use the skull fracture dataset provided by the hospital to verify the proposed method. The experimental results show that the proposed detection method outperforms the current mainstream object detection methods based on deep learning in detection accuracy and recalling rates.