Breast cancer diagnosis relies heavily on histopathology image classification. This study proposes a novel approach leveraging Hybrid EfficientNet models integrated with advanced attention mechanisms (CB and deformable attention) to enhance feature extraction and focus on relevant tissue regions. Evaluating on the BreakHis dataset across multiple magnification scales (40X, 100X, 200X, 400X), we achieve state-of-the-art performance with EfficientNetV2-XL and CB, reaching 98.96% accuracy and 98.31% F1-score at 400X. Integration of CLAHE preprocessing and optimized computational efficiency demonstrates suitability for real-time clinical deployment.

翻译：乳腺癌诊断高度依赖于组织病理学图像分类。本研究提出一种创新方法，利用集成先进注意力机制（CB与可变形注意力）的混合EfficientNet模型，以增强特征提取能力并聚焦相关组织区域。通过在BreakHis数据集上对多种放大倍数（40X、100X、200X、400X）进行评估，我们采用EfficientNetV2-XL与CB注意力机制实现了最先进的性能，在400X放大倍数下达到98.96%的准确率与98.31%的F1分数。CLAHE预处理技术的集成与计算效率的优化，证明了该方法适用于实时临床部署。