HQCNN: A Hybrid Quantum-Classical Neural Network for Medical Image Classification
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866912739331407872 |
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| author | Shahjalal Fahim, Jahid Karim Paul, Pintu Chandra Hossain, Md Robin Ahmed, Md. Tofael Chakraborty, Dulal |
| author_facet | Shahjalal Fahim, Jahid Karim Paul, Pintu Chandra Hossain, Md Robin Ahmed, Md. Tofael Chakraborty, Dulal |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_14277 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | HQCNN: A Hybrid Quantum-Classical Neural Network for Medical Image Classification Shahjalal Fahim, Jahid Karim Paul, Pintu Chandra Hossain, Md Robin Ahmed, Md. Tofael Chakraborty, Dulal Quantum Physics Image and Video Processing 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. |
| title | HQCNN: A Hybrid Quantum-Classical Neural Network for Medical Image Classification |
| topic | Quantum Physics Image and Video Processing |
| url | https://arxiv.org/abs/2509.14277 |