Image processing and machine learning-based bone fracture detection and classification using X-ray images

One of the most important problems in orthopedics is undiagnosed or misdiagnosed bone fractures. This can lead to patients receiving an incorrect diagnosis or treatment, which can result in a longer treatment period. In this study, fracture detection and classification are performed using various machine learning techniques using of a dataset containing various bones (normal and fractured). Firstly, the X-ray images obtained are subjected to image preprocessing stages and are prepared for the feature extraction stage. Then, in addition to the Canny and Sobel edge detection methods used in the image processing stage, feature extraction of X-ray images is performed with the help of Houhg line detection and Harris corner detector. The data obtained by performing feature extraction is given to 12 different machine learning classifiers and the results are presented. Setting hyperparameters for classifiers is done by the grid search method, and the study is tested for 10-fold cross-validation. Classifier results are presented comparatively as accuracy, training time, and testing time, and linear discriminant analysis (LDA) reaches the highest accuracy rate with 88.67% and 0.89 AUC. The proposed computer-aided diagnosis system (CAD) will reduce the burden on physicians by identifying fractures with high accuracy.