Medical Image Classification using Hybrid Quantum-Classical Neural Network

Abstract

Medical image classification plays a crucial role in healthcare, enabling accurate disease diagnosis and treatment planning. Traditional deep learning models, such as Convolutional Neural Networks (CNNs), have demonstrated remarkable success in this domain but are often constrained by high computational costs, data inefficiencies, and challenges in generalization, particularly for small and imbalanced medical datasets. Quantum computing has emerged as a promising alternative, offering computational advantages through quantum parallelism and high-dimensional feature representations. This project explores the integration of quantum computing with classical deep learning to develop a Hybrid Quantum-Classical Neural Network (HQCNN) for binary medical image classification. The proposed model leverages a classical CNN for feature extraction and a quantum circuit for enhanced feature transformation, optimizing classification performance while reducing computational overhead. The model is evaluated on four benchmark medical datasets: BloodMNIST, OrganAMNIST, and PathMNIST. Experimental results demonstrate that the HQCNN model achieves 99.12% accuracy and 99.91% AUC on BloodMNIST, 98.84% accuracy and 99.00% AUC on OrganAMNIST, and 97.83% accuracy with 100.00% AUC on PathMNIST, outperforming traditional CNNs in most cases. While the proposed HQCNN model enhances classification accuracy and robustness, challenges such as quantum noise, limited qubit scalability, and training convergence issues remain significant obstacles. Future research will focus on optimizing quantum circuit architectures, improving hybrid training strategies, and scaling the model for high-resolution medical imaging applications. This study highlights the potential of quantum-assisted deep learning in medical imaging and paves the way for future advancements in quantum-based healthcare solutions