BoneCareMapper: A Framework for Predictive Bone Fracture Detection using Medical Image Analysis

Bone fractures are a critical medical condition requiring early and accurate detection to prevent complications and ensure timely treatment. Conventional X-ray analysis relies heavily on manual interpretation, which is time-consuming and prone to errors. This study proposes BoneCareMapper, a deep learning-based framework for automated bone fracture detection using medical image analysis. The proposed model integrates ResNet-50, Squeeze-and-Excitation (SE) Networks, and Deep Cascade Forest (gcForest) to enhance feature extraction and classification accuracy. Additionally, advanced preprocessing techniques, including Adaptive Contrast Enhancement (ACE), Hybrid Noise Reduction, and Region of Interest (ROI) Segmentation, are applied to improve fracture visibility. The experimental results demonstrate that BoneCareMapper achieves an outstanding accuracy of 97.91%, significantly outperforming existing architectures such as CNN (82.6%), ResNet-50 (89.2%), and EfficientNet-B3 (92.7%). The model also achieves a high AUC-ROC score of 98.6%, ensuring reliable fracture classification. The framework is computationally efficient, with an inference speed of 32ms per sample, making it ideal for real-time medical applications. BoneCareMapper’s superior performance, coupled with its lightweight implementation, ensures seamless deployment in clinical environments, reducing diagnostic errors and enhancing patient outcomes.