Due to the complex mechanism of ankle injury, the clinical diagnosis of ankle fracture is extremely difficult. In order to simplify the fracture diagnosis process, this study proposes an automatic diagnosis model of ankle fractures. Firstly, an ankle fracture classification method suitable for machine learning was developed. By dividing six fracture regions, multiple types of fractures were clarified, and a corresponding dataset was created accordingly. Secondly, the random forest model was used to preprocess the X-ray images and segment the fracture foreground part of the X-ray images. Finally, the bag of visual words (BoVW) was used for feature extraction, and classifiers were constructed in different regions for classification. The area under the curve (AUC) of XGBoost was 0.92 +/- 0.06. The performance of the XGBoost has proved to be better compared with the SVM when training on a small dataset in each region.
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