# HQCNN: A Hybrid Quantum-Classical Neural Network for Medical Image Classification

Source: [HQCNN: A Hybrid Quantum-Classical Neural Network for Medical Image Classification](<https://www.sota2.com/research/paper/68fa4bd41e209131ce948bda>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-12-03T05:01:29.481Z

Authors: Shahjalal, Jahid Karim Fahim, Pintu Chandra Paul, Md Robin Hossain, Md. Tofael Ahmed, Dulal Chakraborty

Published: 2025-09-16T08:02:53.000Z

## Abstract

Classification of medical images plays a vital role in medical image analysis; however, it remains challenging due to the limited availability of labeled data, class imbalances, and the complexity of medical patterns. To overcome these challenges, we propose a novel Hybrid Quantum-Classical Neural Network (HQCNN) for both binary and multi-class classification. The architecture of HQCNN integrates a five-layer classical convolutional backbone with a 4-qubit variational quantum circuit that incorporates quantum state encoding, superpositional entanglement, and a Fourier-inspired quantum attention mechanism. We evaluate the model on six MedMNIST v2 benchmark datasets. The HQCNN consistently outperforms classical and quantum baselines, achieving up to 99.91% accuracy and 100.00% AUC on PathMNIST (binary) and 99.95% accuracy on OrganAMNIST (multi-class) with strong robustness on noisy datasets like BreastMNIST (87.18% accuracy). The model demonstrates superior generalization capability and computational efficiency, accomplished with significantly fewer trainable parameters, making it suitable for data-scarce scenarios. Our findings provide strong empirical evidence that hybrid quantum-classical models can advance medical imaging tasks.

## Recorded benchmark results

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## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2509.14277>)

[PDF](<https://arxiv.org/pdf/2509.14277.pdf>)