Breast Cancer Image Classification Method Based on Deep Transfer Learning
Weimin Wang, Y Li, Xu Yan, Mingxuan Xiao, Min Gao
Abstract
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 Densely Connected Convolutional Networks (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.