关键词: 卷积神经网络, 乳腺钼靶图像, 低级特征, 高级特征, 特征融合

Abstract:

As one of the most common cancers for women, the breast cancer deadly threatens patients’s health, which make multiple classification of mammorgraphy image an important role in clinical diagnosis of breast cancer. Multiple classification research of classical Convolutional Neural Network（CNN） on the breast mammorgraphy image directly takes use of its advanced features. Such method is not accurate enough. While the construction of human network model increases the accuracy of classification. It functions with extracting low-level features of images from stacked convolutional layer and maximum pooling layer, and gradually passing them back to images. By extracting high-level features to cascade the lower one, features of cascading appear, which will be finally classified after being pooled through the global maximum pooling layer. In accordance with a simulation experiment on 1,824 breast mammorgraphy images by Cancer Center, Sun Yat-sen University, the construction of human network model rises accuracy rate to 74.54%, that make it way better than relevant net work models.

Key words: Convolutional Neural Network（CNN）, mammography image, low-level feature, advanced feature, feature fusion