Noisy Mammogram Classification Method Based on New Weighted Fusion Framework

Abstract

Convolutional neural network (CNN) has made outstanding performance in the classification of natural light images. However, images in many fields have the characteristics of high noise, low resolution, no color information and small data set, such as mammogram, which will affect the accuracy and robustness of the model. In order to improve the classification accuracy and the noise robustness of convolution network for mammogram images, we design a novel classification model based on the new weighted fusion convolution framework. This method has been improved from the following aspects: firstly, we take the place of traditional max-pooling layer with convolution layer with increased step, which achieves the purpose of down-sampling and extracts features more rationally through back-propagation. Secondly, we fuse multi-level feature maps to make full use of the information contained in the shallow levels and deep levels. At the same time, we design a new fusion method to effectively fuse the feature maps from different layers with different sizes. Finally, our model is tested on the mammographic image analysis society (MIAS), which is a mammographic medical image dataset. The experimental results show that the average accuracy of the model is as high as 97.6%, and the convolution layer with increased step has better robustness than the traditional max-pooling layer.