Abstract: Breast cancer is the most common malignant disease in women endangering women's life and health. Histopathological analysis is the most extensive method for the diagnosis of breast cancer. The diagnosis and classification of numerous pathological images require pathological experts to visually identify histological samples by microscopy, which takes time and effort to identify features. According to the different magnification of BreaKHis data set of breast cancer pathological images, it can be divided into benign and malignant samples for recognition. A breast cancer image recognition method based on DenseNet network and transfer learning is proposed to compare with VGGNet, ResNet, DenseNet and other deep learning models. The proposed model has strong learning ability and the best recognition effect, and the accuracy of the model can reach more than 98% on this dataset.

Key words: breast cancer images; , DenseNet network; , transfer learning; , deep Learning