关键词: 乳腺癌病理图像分类, 深度学习, 卷积神经网络, 迁移学习, 数据增强

Abstract: The auto-classification has a significant clinical value for breast cancer histopathological image. The conventional image classification method based on hand-crafted features is professional knowledge demanding and time-consuming. Besides, the conventional method can not extract discriminative features in most cases. To this end, this paper proposes an improved deep convolutional neural network model to realize automatic histopathological image classification with higher accuracy. Data augmentation and transfer learning are also adopted to avoid the overfitting by the limitation of training samples. The experimental results show that the recognition rate rises up to 91% and the proposed method has good robustness and generalization.

Key words: breast cancer histopathological image classification, deep leaning, convolutional neural network, transfer learning, data augmentation