Computer Diagnostics of Mammograms Based on Features Extracted Using Deep Learning

Abstract—The main task of the study is to improve the performance of existing computer diagnostic systems using new methods for classification of benign and malignant tumors using digital mammograms. Methods and algorithms for systems of computer diagnostics are being actively developed using deep neural networks. To achieve better results on the selected data set, we transform the data using autoencoders to obtain features with low intraclass and high interclass variance. The entire working cycle of the system consists of the following stages: extraction of features using a segmented part of the pathology, division of the data into two clusters, and feature transformations using linear discriminant analysis for minimization of intraclass variance and classification of pathologies. The results of this study show that the classification of pathologies using deep learning methods makes it possible to achieve high results.