Hybrid Neural Network for Classification of Mammography Images

Abstract—An important step in solving the problem of classification and segmentation of 2D images is the extraction of local geometric features. Convolutional neural networks were widely used in recent years to solve problems in this field. Typically, the neighborhood of each pixel in an image is used to collect local geometric information. A convolutional neural network is used to extract the underlying geometric features of the neighborhood. In this work, we propose a neural network based on descriptor concatenation for two well-known neural networks to solve the problem of extracting local geometric features of mammographic images. To improve the accuracy of mammogram classification, feature filtering is used based on the calculation of joint information. Results of computer simulation are presented to illustrate the performance of the proposed method.