In biomedical engineering, artificial intelligence has become a pivotal tool for enhancing medical diagnostics, particularly in medical image classification tasks such as detecting pneumonia from chest X-rays and breast cancer screening. However, real-world medical datasets frequently exhibit severe class imbalance, where positive samples substantially outnumber negative samples, leading to biased models with low recall rates for minority classes. This imbalance not only compromises diagnostic accuracy but also poses clinical misdiagnosis risks. To address this challenge, we propose SDA-QEC (Simplified Diffusion Augmentation with Quantum-Enhanced Classification), an innovative framework that integrates simplified diffusion-based data augmentation with quantum-enhanced feature discrimination. Our approach employs a lightweight diffusion augmentor to generate high-quality synthetic samples for minority classes, rebalancing the training distribution. Subsequently, a quantum feature layer embedded within MobileNetV2 architecture enhances the model's discriminative capability through high-dimensional feature mapping in Hilbert space. Comprehensive experiments on coronary angiography image classification demonstrate that SDA-QEC achieves 98.33% accuracy, 98.78% AUC, and 98.33% F1-score, significantly outperforming classical baselines including ResNet18, MobileNetV2, DenseNet121, and VGG16. Notably, our framework simultaneously attains 98.33% sensitivity and 98.33% specificity, achieving a balanced performance critical for clinical deployment. The proposed method validates the feasibility of integrating generative augmentation with quantum-enhanced modeling in real-world medical imaging tasks, offering a novel research pathway for developing highly reliable medical AI systems in small-sample, highly imbalanced, and high-risk diagnostic scenarios.

翻译：在生物医学工程领域，人工智能已成为提升医学诊断能力的关键工具，尤其在医学影像分类任务中，例如通过胸部X光片检测肺炎以及乳腺癌筛查。然而，现实世界中的医学数据集常常表现出严重的类别不平衡问题，即阳性样本数量远多于阴性样本，导致模型产生偏差，对少数类别的召回率较低。这种不平衡不仅损害诊断准确性，还带来临床误诊风险。为应对这一挑战，我们提出了SDA-QEC（简化扩散增强与量子增强分类），这是一个创新框架，将基于简化扩散的数据增强与量子增强的特征判别相结合。我们的方法采用轻量级扩散增强器为少数类别生成高质量的合成样本，从而重新平衡训练分布。随后，嵌入在MobileNetV2架构中的量子特征层通过希尔伯特空间中的高维特征映射，增强了模型的判别能力。在冠状动脉造影图像分类上的全面实验表明，SDA-QEC实现了98.33%的准确率、98.78%的AUC和98.33%的F1分数，显著优于包括ResNet18、MobileNetV2、DenseNet121和VGG16在内的经典基线模型。值得注意的是，我们的框架同时达到了98.33%的敏感性和98.33%的特异性，实现了对临床部署至关重要的平衡性能。所提出的方法验证了在真实世界医学影像任务中整合生成式增强与量子增强建模的可行性，为在小样本、高度不平衡及高风险诊断场景中开发高可靠性的医学人工智能系统提供了一条新颖的研究路径。