Abstract
Accurate detection of bone fractures in radiographic images is a critical yet challenging task in medical diagnosis due to subtle visual cues and the need for region-specific interpretation. This study proposes a two-stage deep learning pipeline for multi-region fracture detection and classification in X-ray images. In the first stage, the Bone Fracture dataset is employed to localize anatomical regions with bounding box annotations and to detect the presence of fractures. In the second stage, the Bone Break Classification dataset is used to categorize fracture subtypes, including transverse, oblique, and avulsion. The pipeline integrates these heterogeneous datasets to first identify the anatomical region and fracture presence, and then classify the fracture type when detected. Models were developed using EfficientNetB0 backbones trained in TensorFlow/Keras, with preprocessing steps such as resizing, normalization, one-hot encoding, and augmentation. Experimental results showed a detection accuracy of more than 90% for region-level fracture identification and a classification accuracy of More than 90% for fracture subtypes, with F1-scores closely aligned. Grad-CAM visualizations further confirmed the interpretability of the learned features. These findings demonstrate that combining region-based and type-based datasets enhances robustness and clinical relevance, paving the way for reliable decision-support tools in medical imaging.