Automated Bone Fracture Classification Using a Hybrid Quantum-Classical Neural Network Approach

This paper presents a novel hybrid quantum-classical model for automated bone fracture detection and classification from radiographic images. By integrating quantum computing principles with convolutional neural networks, we demonstrate a system capable of achieving unprecedented accuracy in fracture detection. The proposed methodology leverages quantum-enhanced feature extraction while maintaining clinical deployment feasibility. Our approach utilizes a quantum-augmented VGG16 architecture with specialized quantum circuits designed to improve feature space exploration. with our hybrid model achieving 100% accuracy on the test dataset. This research establishes a new paradigm for medical image analysis that harnesses quantum computational advantages within existing classical frameworks.