Breast abnormalities classification using deep learning feature extraction

Vladislav Pryadka; Andrei Krendal; Jose A. Gonzalez-Fraga; Vitaly Kober; Anastasia Kober

doi:10.1117/12.2677167

4 October 2023 Breast abnormalities classification using deep learning feature extraction

Vladislav Pryadka, Andrei Krendal, Jose A. Gonzalez-Fraga, Vitaly Kober, Anastasia Kober

Proceedings Volume 12674, Applications of Digital Image Processing XLVI; 126741M (2023) https://doi.org/10.1117/12.2677167
Event: SPIE Optical Engineering + Applications, 2023, San Diego, California, United States

Conference Poster

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.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Vladislav Pryadka, Andrei Krendal, Jose A. Gonzalez-Fraga, Vitaly Kober, and Anastasia Kober "Breast abnormalities classification using deep learning feature extraction", Proc. SPIE 12674, Applications of Digital Image Processing XLVI, 126741M (4 October 2023); https://doi.org/10.1117/12.2677167

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available