Despite the remarkable results of deep learning in breast cancer image classification, challenges such as data imbalance and interpretability still exist and require cross-domain knowledge and collaboration among medical experts. In this study, we propose a dual-activated lightweight attention ResNet50 module method-based breast cancer classification method that effectively addresses challenges such as data imbalance and interpretability. Our model fuses a pre-trained deep ResNet50 and a lightweight attention mechanism to accomplish classification by embedding an attention module in layer 4 of ResNet50 and adding two fully connected layers. For the fully connected network design, we employ both Leaky ReLU and ReLU activation functions. On medical histopathology datasets, our model outperforms conventional models, visual transformers, and large models in terms of precision, accuracy, recall, F1 score, and GMean. In particular, the model demonstrates significant robustness and broad applicability when dealing with the unbalanced breast cancer dataset. Our model is tested on 40X, 100X, 200X, and 400X images and achieves accuracies of 98.5%, 98.7%, 97.9%, and 94.3%, respectively. Through an in-depth analysis of loss and accuracy, as well as Grad-CAM analysis, we comprehensively assessed the model performance and gained perspective on its training process. In the later stages of training, the validated losses and accuracies change minimally, showing that the model avoids overfitting and exhibits good generalization ability. Overall, this study provides an effective solution for breast cancer image classification with practical applica

翻译：尽管深度学习在乳腺癌图像分类中取得了显著成果，但数据不平衡和可解释性等挑战依然存在，需要跨领域知识及医学专家的协作。本研究提出一种基于双激活轻量注意力ResNet50模块的乳腺癌分类方法，有效解决了数据不平衡和可解释性等问题。该模型融合了预训练的深度ResNet50与轻量注意力机制，通过在ResNet50第4层嵌入注意力模块并添加两个全连接层实现分类。在全连接网络设计中，我们同时采用了Leaky ReLU和ReLU激活函数。在医学组织病理学数据集上，本模型在精确率、准确率、召回率、F1分数和GMean指标上均优于传统模型、视觉Transformer及大型模型。尤其在处理乳腺癌不平衡数据集时，模型展现出显著的鲁棒性和广泛适用性。我们在40倍、100倍、200倍和400倍放大图像上测试模型，分别达到了98.5%、98.7%、97.9%和94.3%的准确率。通过损失与准确率的深入分析及Grad-CAM分析，我们全面评估了模型性能并洞察其训练过程。在训练后期，验证损失与准确率变化极小，表明模型避免了过拟合，具有良好泛化能力。总体而言，本研究为乳腺癌图像分类提供了具有实际应用价值的有效解决方案。