Exploratory study on fracture identification in hand radiographs using deep and machine learning methods

Modified abstractBone fractures of the hand are frequently missed in radiographic examinations due to their complex anatomy and subtle fracture patterns. This study proposes a hybrid deep learning–machine learning (DL–ML) framework for automated hand fracture detection using X-ray images. Pre-trained convolutional neural networks—ResNet-18, SqueezeNet, and AlexNet—were employed as feature extractors, while six machine learning classifiers, including Support Vector Machines, K-Nearest Neighbour, and Discriminant Analysis, were used for classification. Bayesian optimisation was applied to tune hyperparameters and enhance performance. The best results were achieved using ResNet-18 features combined with a KNN classifier, yielding an accuracy of 99.86%, precision of 0.997, recall of 0.997, and an F1-score of 0.997. In comparison, end-to-end deep learning models performed significantly worse, with AlexNet achieving a maximum accuracy of 78.06%. The results demonstrate that hybrid DL–ML approaches outperform standalone deep learning models and offer a lightweight, accurate solution to assist radiologists in reliable hand fracture diagnosis.