Cancer detection in breast histopathology with convolution neural network based approach

Breast cancer is one of most common causes of mortality in women. However, few limitations, e.g., similar structure statistics in inter-class and textural variations in intra-class images make the breast histology analysis a challenging process. In this paper, the multi-class breast cancer classification is carried out with deep convolution neural network (CNN) based transfer learning approach. To explore the feasibility of transfer learning in breast histology, pre-trained deep CNN model is inherited and simultaneously a multi-scale feature concatenation strategy is used. Moreover, incorporating with stain normalization and channel color modification strategies the designed model can be effectively trained. The experiments on publicly available multi-class ICIAR 2018 breast dataset corroborated the efficiency of ou method. The designed approach outperforms the existing methods by achieving 94.3% and 97.5% accuracy on 4-class and 2-class histology image recognition respectively.