Computer Engineering and Applications ›› 2020, Vol. 56 ›› Issue (8): 166-170.

### Classification of Mammography Based on Attention Mechanism

SHENG Longshuai, LI Ce, LI Xin

1. 1.Jiangsu Key Laboratory of Image and Video Understanding for Social Safety, Nanjing University of Science and Technology, Nanjing 210094, China
2.School of Mechanical Electronic and Information Engineering, China University of Mining and Technology（Beijing）, Beijing 100083, China
• Online:2020-04-15 Published:2020-04-14

### 基于注意力机制的乳腺X线摄影分类方法

1. 1.江苏省社会安全图像与视频理解重点实验室（南京理工大学），南京 210094
2.中国矿业大学（北京） 机电与信息工程学院，北京 100083

Abstract:

The worsening environment has led to an increase in the incidence of cancer. In 2018, the incidence of breast cancer has ranked first among all cancers in the world. Considering that mammography is affordable and easy to operate, it is currently regarded as the best screening method for breast cancer and the most effective method for early detection of breast cancer. In view of the fact that mammography is not easy to distinguish and its features are not obvious, this paper proposes an attention memory network based on RNN + CNN to classify it. The attention memory network includes the attention memory module and the convolution residual module. In the attention memory module, the attention module is used to extract features of mammography, and the memory module adds attention weight to RNN network to simulate people’s emphasis on key information. Convolution residual module uses CNN to classify images. The contributions of this paper are as follows, the attention memory network is proposed for mammography image classification, the designed network introduces attention weights on the RNN+CNN structure, and extracts key information of images to enhance the feature description. The experimental results on the mammography INbreast dataset show that the runtime of attention memory network can be 50% less than the pre-trained Inception v2, ResNet50, and VGG16, and can achieve higher classification accuracy than others.