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.

翻译：乳腺组织病理学图像分类对于早期癌症检测至关重要，通过及时诊断有望降低死亡率。本文提出一种新颖方法，将混合高效网络模型与先进注意力机制相结合，包括卷积块注意力模块（CBAM）、自注意力机制和可变形注意力机制，以增强特征提取并聚焦关键图像区域。我们使用公开可用的组织病理学数据集，在多种放大倍数下评估模型性能。该方法取得了显著改进，在400倍放大倍数下准确率达到98.42%，超越了包括VGG和ResNet架构在内的多种先进模型。通过准确率、F1分数、精确率和召回率等指标验证结果，证明了该模型在提高诊断准确性方面的临床潜力。此外，所提方法展现出更高的计算效率，适合集成到实时诊断工作流程中。