Sun Yuyang, Deng Xing, Shao Haijian
School of Computer, Jiangsu University of Science and Technology Zhenjiang 212100, Jiangsu, China.
Am J Transl Res. 2025 May 15;17(5):3357-3367. doi: 10.62347/WOHQ8174. eCollection 2025.
Accurate classification of skin cancer is critical for early detection and timely treatment, significantly improving patient survival rates. While quantum neural networks combined with transfer learning show promise in medical image analysis, quantum noise remains a major challenge, compromising the stability and reliability of these systems. This study aims to address this limitation by developing a robust quantum-based framework for skin cancer classification.
We propose a Quantum Dual-Branch Neural Network (QDBNN) that employs two independently trained network branches without shared weights. Dual-modal features are fused at the fully connected layer, and a Variational Quantum Classifier (VQC) is utilized for final classification. The model is evaluated on two datasets: the multiclass HAM10000 and the binary Malignant vs. Benign dataset.
QDBNN achieved state-of-the-art accuracies of 93.6% on HAM10000 and 93.5% on the Malignant vs. Benign dataset, outperforming classical and quantum transfer learning baselines. The dual-branch architecture and weighted feature fusion demonstrated enhanced robustness against quantum noise while improving generalization.
QDBNN effectively mitigates quantum noise interference and leverages quantum-classical hybrid advantages for skin cancer classification. Its success highlights the potential of quantum-inspired architectures in medical imaging, offering a pathway toward clinically deployable tools for early diagnosis. Future work will focus on hardware optimization and scalability to larger datasets.
皮肤癌的准确分类对于早期检测和及时治疗至关重要，可显著提高患者生存率。虽然量子神经网络与迁移学习相结合在医学图像分析中显示出前景，但量子噪声仍然是一个重大挑战，会损害这些系统的稳定性和可靠性。本研究旨在通过开发一种用于皮肤癌分类的强大的基于量子的框架来解决这一局限性。
我们提出了一种量子双分支神经网络（QDBNN），它采用两个独立训练且无共享权重的网络分支。在全连接层融合双模态特征，并使用变分量子分类器（VQC）进行最终分类。该模型在两个数据集上进行评估：多类别HAM10000数据集和恶性与良性二元数据集。
QDBNN在HAM10000数据集上达到了93.6%的先进准确率，在恶性与良性数据集上达到了93.5%，优于经典和量子迁移学习基线。双分支架构和加权特征融合在提高泛化能力的同时，展现出了对量子噪声更强的鲁棒性。
QDBNN有效减轻了量子噪声干扰，并利用量子 - 经典混合优势进行皮肤癌分类。其成功凸显了量子启发架构在医学成像中的潜力，为早期诊断的临床可部署工具提供了一条途径。未来的工作将集中在硬件优化和对更大数据集的可扩展性上。
