  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) model. 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 reported results of DALAResNet50 have been shown to outperform
the compared approaches regarding accuracy, F1 score, IBA, and GMean,
demonstrating significant robustness and broad applicability when dealing with
different magnifications and imbalanced breast cancer datasets
