Classification of Breast Abnormalities Using Deep Learning

—Early detection of breast abnormalities through mammography screening and proper treatment reduces mortality and increases women’s life expectancy. Currently, methods and algorithms for computer diagnostic systems based on deep neural networks are being actively developed. Such systems combine sele...

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Bibliographic Details
Published in:Journal of communications technology & electronics 2022-12, Vol.67 (12), p.1552-1556
Main Authors: Gomina, P. S., Kober, V. I., Karnaukhov, V. N., Mozerov, M. G., Kober, A. V.
Format: Article
Language:English
Subjects:
Abnormalities
Algorithms
Artificial neural networks
Classification
Communications Engineering
Decision making
Deep learning
Diagnostic systems
Engineering
Health aspects
Life expectancy
Machine learning
Mammography
Networks
Neural networks
Theory and Methods of Information Processing
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Summary:—Early detection of breast abnormalities through mammography screening and proper treatment reduces mortality and increases women’s life expectancy. Currently, methods and algorithms for computer diagnostic systems based on deep neural networks are being actively developed. Such systems combine selection, feature calculation, and classification, thereby directly creating a decision-making function. In this paper, a method for classifying breast pathologies according to the Breast Imaging Reporting and Data System (BI-RADS) based on deep learning is proposed. Experimental results are presented using two open databases of digital mammography and evaluated using various performance criteria.
ISSN:1064-2269
1555-6557
DOI:10.1134/S1064226922120051