Deep Learning Model Based Breast Cancer Histopathological Image Classification

The automatic and precision classification for breast cancer histopathological image has a great significance in clinical application. However, the existing analysis approaches are difficult to addressing the breast cancer classification problem because the feature subtle differences of inter-class histopathological image and the classification accuracy still hard to meet the clinical application. Recent advancements in data-driven sharing processing and multi-level hierarchical feature learning have made available considerable chance to dope out a solution to this problem. To address the challenging problem, we propose a novel breast cancer histopathological image classification method based on deep convolutional neural networks, named as BiCNN model, to address the two-class breast cancer classification on the pathological image. This deep learning model considers class and sub-class labels of breast cancer as prior knowledge, which can restrain the distance of features of different breast cancer pathological images. In addition, an advanced data augmented method is proposed to fit tolerance whole slide image recognition, which can full reserve image edge feature of cancerization region. The transfer learning and fine-tuning method are adopted as an optimal training strategy to improve breast cancer histopathological image classification accuracy. The experiment results show that the proposed method leads to a higher classification accuracy (up to 97%) and displays good robustness and generalization, which provides efficient tools for breast cancer clinical diagnosis.