Breast Cancer Image Classification Method Based on Deep Transfer
  Learning
Weimin Wang Min Gao Mingxuan Xiao Xu Yan Yufeng Li
To address the issues of limited samples, time-consuming feature design, and
low accuracy in detection and classification of breast cancer pathological
images, a breast cancer image classification model algorithm combining deep
learning and transfer learning is proposed. This algorithm is based on the
DenseNet structure of deep neural networks, and constructs a network model by
introducing attention mechanisms, and trains the enhanced dataset using
multi-level transfer learning. Experimental results demonstrate that the
algorithm achieves an efficiency of over 84.0\% in the test set, with a
significantly improved classification accuracy compared to previous models,
making it applicable to medical breast cancer detection tasks.