1(School of Electronic and Information Engineering, Nanjing University of Information Science and Technology, Nanjing 210044, China) 2(Department of Radiation Oncology, Jinling Hospital, School of Medicine Nanjing University, Nanjing 210002, China)
Abstract：In view of the extremely inadequate extraction of pathological features from existing models and the unbalanced number of various types of open breast cancer data sets, the research on multi-classification of breast cancer pathological images is still challenging. In this paper, an improved residual network multi-classification method of breast cancer pathological images based on multi-scale feature fusion was proposed. Firstly, based on ResNet101 residual network, the CBAM attention module was inserted into each residual block. Next, in order to optimize feature extraction, horizontal and vertical multi-scale feature fusion was integrated into the residual network. Finally, the focus loss function was introduced to solve the problem of unbalanced data distribution. Validated by the training of 1582 pathology images with mixed magnifications on BreakHis public dataset, the proposed improved residual network achieved a recognition accuracy of 94.4% on eight classifications of breast cancer pathology images, which was 2.8% better than the original model and outperforms most of the existing publicly available deep learning models. The proposed model provided a more effective method for screening, diagnosis and pathological classification of female breast cancer.
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