Automatic breast cancer classification in histopathology images is crucial for precise diagnosis and treatment planning. Recently, classification approaches based on the ResNet architecture have gained popularity for significantly improving accuracy by using skip connections to mitigate vanishing gradient problems, thereby integrating low-level and high-level feature information. Nevertheless, the conventional ResNet architecture faces challenges such as data imbalance and limited interpretability, necessitating cross-domain knowledge and collaboration among medical experts. This study effectively addresses these challenges by introducing a novel method for breast cancer classification, the Dual-Activated Lightweight Attention ResNet50 (DALAResNet50). It integrates a pre-trained ResNet50 model with a lightweight attention mechanism, embedding an attention module in the fourth layer of ResNet50 and incorporating two fully connected layers with LeakyReLU and ReLU activation functions to enhance feature learning capabilities. The DALAResNet50 method was tested on breast cancer histopathology images from the BreakHis Database across magnification factors of 40X, 100X, 200X, and 400X, achieving accuracies of 98.5%, 98.7%, 97.9%, and 94.3%, respectively. It was also compared with established deep learning models such as SEResNet50, DenseNet121, VGG16, VGG16Inception, ViT, Swin-Transformer, Dinov2_Vitb14, and ResNet50. The results demonstrate that DALAResNet50 surpasses these models in precision, accuracy, recall, F1 score, and GMean, proving its robustness and applicability across various magnifications and handling imbalanced breast cancer datasets.

翻译：组织病理学图像中的乳腺癌自动分类对于精准诊断和治疗规划至关重要。近年来，基于ResNet架构的分类方法因通过跳跃连接缓解梯度消失问题，从而融合低层与高层特征信息并显著提升准确率而广受关注。然而，传统ResNet架构面临数据不平衡和可解释性受限等挑战，需要跨领域知识及医学专家协作。本研究通过引入一种新颖的乳腺癌分类方法——双重激活轻量注意力ResNet50（DALAResNet50），有效解决了上述挑战。该方法将预训练的ResNet50模型与轻量注意力机制相结合，在ResNet50第四层嵌入注意力模块，并采用两个全连接层分别配合LeakyReLU和ReLU激活函数以增强特征学习能力。DALAResNet50在BreakHis数据库的乳腺癌组织病理学图像上进行了测试，覆盖40X、100X、200X和400X放大倍数，分别达到98.5%、98.7%、97.9%和94.3%的准确率。此外，该方法与SEResNet50、DenseNet121、VGG16、VGG16Inception、ViT、Swin-Transformer、Dinov2_Vitb14及ResNet50等经典深度学习模型进行了对比。结果表明，DALAResNet50在精确率、准确率、召回率、F1分数和G均值上均超越这些模型，证明了其在不同放大倍数下的鲁棒性、适用性及处理不平衡乳腺癌数据集的能力。