Bone fractures are among the most common global health problems and require rapid and accurate diagnosis to prevent complications and reduce healthcare costs. Traditional diagnostic methods rely on the manual examination of X-ray images by experienced radiologists, a process that is time-consuming and prone to human error, especially in emergency departments with high patient volumes. This study proposes a lightweight hybrid approach that integrates deep learning–based feature extraction with classical machine learning algorithms for automatic bone fracture detection. The dataset used comprises 4,840 fractured and 4,623 non-fractured bone X-ray images obtained from the Kaggle platform, in which data augmentation was previously applied by the dataset developer. A compact convolutional neural network architecture, MobileNetV2, was employed exclusively for deep feature extraction. The extracted feature vectors were then used to train and evaluate five ensemble learning algorithms: Random Forest, Extra Trees, Gradient Boosting, AdaBoost, and Bagging Classifier. Model performance was assessed using 10-fold cross-validation based on accuracy, precision, recall (sensitivity), and F-score metrics. Experimental results show that the Extra Trees algorithm achieved the highest performance with 99.14% accuracy, 99.22% precision, 99.03% recall, and 99.12% F-score, while also yielding the shortest training time of 199 seconds. Random Forest followed with an accuracy of 98.38%. The superior performance and computational efficiency of the Extra Trees algorithm demonstrate its suitability for real-time clinical applications. These findings confirm that combining deep feature extraction with ensemble learning can effectively support automated diagnostic systems for bone fracture detection, enabling healthcare professionals to make faster and more reliable decisions.
