Osteoarthritis Classification Using Hybrid Quantum Convolutional Neural Network

Abstract

This work explores the integration of quantum computing within a classical Convolutional Neural Network (CNN) framework, leveraging a VGG16 model enhanced with a quantum Convolutional Neural Network (QCNN) for the classification of knee osteoarthritis (OA) severity. A custom quantum layer was incorporated to boost the model's feature extraction capabilities, enabling effective differentiation between healthy and OA-affected knees in medical imaging data. The model utilises the Swish activation function, which combines smoothness and nonlinearity to enhance learning efficiency and gradient flow, which are critical in complex diagnostic tasks. Using a QCNN, this model harnesses the ability of quantum computing to represent high-dimensional data transformations, a novel approach that complements classical CNN layers by exploring patterns that are not captured in traditional networks. The initial results showed a high classification accuracy of 97.26%, suggesting that quantum-enhanced layers can significantly bolster feature extraction and classification in medical diagnostics. Furthermore, multiple variations of pre-trained models with Quantum Convolutional Layers, such as XceptionNet, ResNet, and InceptionNet, were developed and compared in terms of the evaluation metrics that achieved 87.84,89.70, and 86.64, respectively. This hybrid VGG16-QCNN model showed the highest accuracy, demonstrating the potential of QCNNs in advancing the precision and scalability of AI-driven healthcare solutions.