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image of A Hybrid Quantum-Enhanced Sandwich Convolutional Neural Network for Medical Image Classification

Abstract

Introduction

Medical image classification is a crucial task in cancer diagnosis, relying on the accurate analysis of high-dimensional imaging data. While Convolutional Neural Networks (CNNs) have shown great success in this domain, their performance is often limited by the shallow feature expressiveness and overfitting, particularly in small or heterogeneous datasets.

Methods

Quantum machine learning offers new opportunities through high-dimensional representations and nonlinear transformations. In this work, we propose a Quantum-Enhanced Sandwich Convolutional Neural Network (QSCNN), a layered hybrid architecture that integrates quantum and classical modules. The model employs a quanvolutional layer for localized quantum feature extraction, followed by conventional convolution and pooling for hierarchical representation learning, and a variational quantum classifier for nonlinear decision-making.

Results

QSCNN achieved higher accuracy and training stability than classical CNNs and QCCNN baselines across three medical imaging tasks.: brain tumor MRI, skin cancer dermoscopy, and lung cancer CT. Circuit depth analysis revealed a trade-off between expressiveness and robustness, and additional experiments with depolarizing noise confirmed the model’s resilience under realistic quantum error conditions.

Discussion

This suggests that circuit design choices influence hybrid model behavior and generalization, supporting the feasibility of quantum-enhanced methods for small-sample medical imaging. However, the current evaluation is limited to relatively small benchmark datasets, and broader validation on large-scale data will be essential to confirm clinical applicability.

Conclusion

In summary, QSCNN presents a feasible hybrid framework for enhancing medical image classification with quantum features. While preliminary, our results suggest potential advantages in accuracy and stability under NISQ conditions.

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2026-01-05
2026-02-04

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A Hybrid Quantum-Enhanced Sandwich Convolutional Neural Network for Medical Image Classification
Bentham Science Publishers ; https://doi.org/10.2174/0115748936418250251104114256
/content/journals/cbio/10.2174/0115748936418250251104114256
/content/journals/cbio/10.2174/0115748936418250251104114256
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  • Article Type:     Research Article
Keywords: quantum machine learning ; convolutional neural network ; medical image classification ; quantum biomedical application ; algorithm ; hybrid quantum-classical ; Quantum computing

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