量子-经典混合模型提升脑肿瘤分类准确率与可解释性
HQCM-EBTC: A Hybrid Quantum-Classical Model for Explainable Brain Tumor Classification
- 用5量子比特并行电路融合量子计算与深度学习
- 96.48%准确率，显著优于传统模型的86.72%
- 注意力图更精准定位肿瘤，适合临床辅助诊断
我们提出HQCM-EBTC，一种用于自动脑肿瘤分类的混合量子-经典模型，基于包含正常、脑膜瘤、胶质瘤和垂体瘤四类共7,576例MRI扫描数据集训练。该模型整合了5量子比特、深度为2的量子层及5条并行电路，采用AdamW优化器和结合交叉熵与注意力一致性的复合损失函数进行优化。实验显示，该模型达到96.48%的准确率，显著优于经典基线模型（86.72%），在胶质瘤检测中精度与F1分数更高。t-SNE投影表明量子空间中特征分离性增强，混淆矩阵显示误分类率降低。注意力图分析（Jaccard Index）证实高置信度下肿瘤定位更准确且聚焦。结果表明，量子增强模型在医学影像中具有提升诊断准确率与可解释性的潜力。
原文摘要 · Abstract (English)
We propose HQCM-EBTC, a hybrid quantum-classical model for automated brain tumor classification using MRI images. Trained on a dataset of 7,576 scans covering normal, meningioma, glioma, and pituitary classes, HQCM-EBTC integrates a 5-qubit, depth-2 quantum layer with 5 parallel circuits, optimized via AdamW and a composite loss blending cross-entropy and attention consistency. HQCM-EBTC achieves 96.48% accuracy, substantially outperforming the classical baseline (86.72%). It delivers higher precision and F1-scores, especially for glioma detection. t-SNE projections reveal enhanced feature separability in quantum space, and confusion matrices show lower misclassification. Attention map analysis (Jaccard Index) confirms more accurate and focused tumor localization at high-confidence thresholds. These results highlight the promise of quantum-enhanced models in medical imaging, advancing both diagnostic accuracy and interpretability for clinical brain tumor assessment.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。