HQCNN: a hybrid quantum-classical neural network with Fourier-inspired quantum attention for medical image classification
Shahjalal Khan1, Jahid Karim Fahim1, Pintu Chanda Paul1, Md Robin Hossain1, Samarjit Saha1, Md Tofael Ahmed1, Dulal Chakraborty1 and Kashmi Sultana1
Corresponding Author : Pintu Chanda Paul
Received : 05-February-2026; Revised : 26-August-2026; Accepted : 28-August-2026
Abstract
Medical image classification is a critical component of modern healthcare; however, accurate diagnosis remains challenging due to limited annotated datasets, class imbalance, and the high dimensionality of medical imaging data. To address these challenges, a hybrid quantum-classical neural network (HQCNN) is proposed, integrating classical deep learning with variational quantum learning for medical image classification. The proposed architecture combines a five-layer convolutional neural network (CNN) for hierarchical feature extraction with a lightweight 4-qubit variational quantum circuit (VQC) incorporating quantum state encoding, superposition and entanglement mechanisms, and a quantum attention-Fourier (QAF) module. This hybrid design aims to improve nonlinear feature representation and quantum parameter efficiency while maintaining a shallow quantum circuit suitable for noisy intermediate-scale quantum (NISQ)-era constraints. Experimental evaluation on six MedMNIST benchmark datasets demonstrated competitive performance across both binary and multi-class classification tasks. HQCNN achieved 98.88% accuracy on the binary subset of PathMNIST (classes 0 vs. 1), 97.61% accuracy on the multi-class OrganAMNIST dataset, and 86.29% accuracy on BreastMNIST. Comparative experiments and statistical analyses demonstrated consistent improvements over the controlled BHQNN baseline, while component-wise ablation studies showed that the QAF module, superposition and entanglement mechanisms, and expressive parameterized rotations contributed to classification performance in a complementary and dataset-dependent manner. Moreover, HQCNN reduced the number of trainable quantum parameters by approximately 55.6% compared with the baseline hybrid quantum neural network (BHQNN). Noise-aware simulations further showed that the model retained relatively stable predictive performance under moderate depolarizing noise, supporting further evaluation under near-term quantum computing conditions. Overall, the results demonstrate that HQCNN provides a parameter-efficient hybrid quantum-classical framework for medical image classification and offers a promising foundation for further investigation of quantum-enhanced medical image analysis.
Keywords
Hybrid quantum-classical neural network, Medical image classification, Variational quantum circuit, Quantum machine learning, Quantum attention-Fourier, MedMNIST.
Cite this article
Khan S, Fahim JK, Paul PC, Hossain MR, Saha S, Ahmed MT, Chakraborty D, Sultana K. HQCNN: a hybrid quantum-classical neural network with Fourier-inspired quantum attention for medical image classification. International Journal of Advanced Technology and Engineering Exploration. 2026;13(141):382-407. DOI : 10.19101/IJATEE.2026.131340108