BONE FRACTURE DETECTION AND DIAGNOSIS USING MACHINE LEARNING

Abstract Bone fractures represent one of the most prevalent orthopedic injuries worldwide, affecting millions annually and imposing substantial burdens on healthcare systems. Traditional diagnosis relies on manual radiological examination, which is time-intensive, subject to inter-observer variability, and demands significant expertise. This paper presents an Advanced Bone Fracture Detection System leveraging an ensemble of state-of-the art Convolutional Neural Networks (CNNs) — DenseNet, EfficientNet, and Vision Transformer (ViT) — to automate fracture detection, classification, severity grading, and anatomical localization across three imaging modalities: X-ray, CT scan, and MRI. Trained on 1,247 annotated images with extensive data augmentation and transfer learning, the system achieves 95.8% binary detection accuracy (AUC-ROC: 0.9832), 93.4% multi-class fracture type classification across seven fracture categories, 97.8% anatomical localization accuracy, and 91.2% severity assessment accuracy. Performance across imaging modalities reaches 96.2% (X-ray), 98.1% (CT), and 94.3% (MRI). The system further generates clinician-ready diagnostic reports with personalized rehabilitation plans, Grad-CAM visualizations, and confidence-calibrated predictions. Cloud GPU deployment achieves ensemble inference in 527 ms, enabling practical clinical integration. Results Amar Student, Department of Computer science and engineering PDA College of engineering Kalaburagi,India process that is time-consuming, prone to fatigue-induced errors, and significantly variable across practitioners. Modern radiology departments process 300–500 imaging studies per day. This volume creates substantial cognitive load, directly correlating with increased diagnostic error rates [2]. Inter-observer variability further complicates consistent care delivery, particularly in multi site hospital networks. In developing nations and rural settings, the shortage of specialist radiologists exacerbates delays in fracture diagnosis, leading to complications including malunion, nonunion, and chronic pain syndromes [3]. Artificial Intelligence (AI), particularly Deep Learning through Convolutional Neural Networks (CNNs), has demonstrated remarkable capabilities in medical image pattern recognition. Prior studies demonstrate CNN systems achieving sensitivity and specificity comparable to experienced radiologists for fracture detection [4], [5]. However, most systems address binary detection on a single imaging modality. Comprehensive multi-modal, multi-task systems integrating detection, classification, severity grading, anatomical localization, and clinical reporting remain underexplored. demonstrate clinical-grade performance comparable to or exceeding published radiologist benchmarks, with substantial potential for deployment in resource-limited healthcare settings.