A HYBRID MULTI-MODAL DEEP LEARNING FRAMEWORK FOR AUTOMATED FRACTURE DETECTION IN RADIOGRAPHS AND CT IMAGES

Bone fractures are among the most prevalent musculoskeletal injuries, necessitating prompt and accurate diagnosis to ensure effective treatment and reduce complications. Traditional fracture detection relies heavily on manual interpretation of X-ray and computed tomography (CT) images by radiologists, which is time-intensive and susceptible to human error, especially in the case of subtle or complex fractures. To address these challenges, this paper proposes FracturaX, a novel hybrid multi-modal deep learning framework designed for automated fracture detection across both X-ray and CT modalities. The proposed architecture integrates handcrafted radiomics features with deep convolutional features through a multi-stream network and an attention-based feature fusion mechanism, enhancing detection accuracy and robustness. The framework was evaluated on diverse datasets, demonstrating superior performance compared to existing single-modality approaches and providing interpretable visual explanations to support clinical decision-making. Experimental results confirm that FracturaX offers a promising step toward reliable, generalizable, and explainable computer-aided fracture diagnosis, potentially reducing diagnostic workload and improving patient outcomes.