Breast abnormalities classification using deep learning feature extraction

Abstract

In this study, the main goal is to improve the performance of existing computer diagnostic systems by proposing new processing methods. We use the public CBIS-DDSM dataset for training and validation. The dataset consists of normal screenings with benign tumors and malignant tumors, with all pathologies carefully selected and checked by a radiologist. The data set also includes ROI masks and pathology bounding boxes, as well as labels corresponding to the class of each pathology diagnosis. To achieve better results on the dataset, we transform the data for their more efficient representation using autoencoders in order to obtain features with low intraclass and high interclass variance, and apply LDA to the encoded features to classify pathologies. Methods for automated pathology detection are not considered in this article, since it is mainly focused on the classification task itself. The entire pipeline of the system consists of the following steps: first, feature extraction using pathology segmentation; dividing the data into two clusters; feature transformation using linear discriminant analysis to minimize intra-class variance; finally, the classification of pathologies. The results of this study for the classification of pathologies using various deep learning methods are presented and discussed.