Classification of Breast Abnormalities Using Deep Learning

被引：4

作者：

Gomina, P. S. ^{[1
]}

Kober, V. I. ^{[1
,2
,4
]}

Karnaukhov, V. N. ^{[2
]}

Mozerov, M. G. ^{[2
]}

Kober, A. V. ^{[3
]}

机构：

[1] Chelyabinsk State Univ, Chelyabinsk 454001, Russia

[2] Russian Acad Sci, Kharkevich Inst Informat Transmiss Problems, Moscow 127051, Russia

[3] Russian Acad Sci, Fed Res Ctr Biol Syst & Agrotechnol, Orenburg 460000, Russia

[4] Ctr Sci Res & Higher Educ, Ensenada 22860, Baja California, Mexico

来源：

JOURNAL OF COMMUNICATIONS TECHNOLOGY AND ELECTRONICS | 2022年 / 67卷 / 12期

基金：

俄罗斯科学基金会;

关键词：

on; digital mammography; U-net deep convolutional neural network; data augmentation; RECOGNITION; DIAGNOSIS;

D O I：

10.1134/S1064226922120051

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

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.

引用

页码：1552 / 1556

页数：5

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