Breast cancer histopathology image classification is crucial for early cancer detection, offering the potential to reduce mortality rates through timely diagnosis. This paper introduces a novel approach integrating Hybrid EfficientNet models with advanced attention mechanisms, including Convolutional Block Attention Module (CBAM), Self-Attention, and Deformable Attention, to enhance feature extraction and focus on critical image regions. We evaluate the performance of our models across multiple magnification scales using publicly available histopathological datasets. Our method achieves significant improvements, with accuracy reaching 98.42% at 400X magnification, surpassing several state-of-the-art models, including VGG and ResNet architectures. The results are validated using metrics such as accuracy, F1-score, precision, and recall, demonstrating the clinical potential of our model in improving diagnostic accuracy. Furthermore, the proposed method shows increased computational efficiency, making it suitable for integration into real-time diagnostic workflows.

本研究解决了乳腺癌组织病理图像分类中的特征提取不足的问题，提出了一种将混合EfficientNet模型与先进注意力机制相结合的新方法。研究表明，该方法在400倍放大下的分类准确率达到98.42%，显著超过现有的多种先进模型，展示了其在提高诊断准确性及实时诊断工作流程整合方面的临床潜力。

基于混合注意力机制的EfficientNet在乳腺组织病理分类中的应用：一项综合研究