Classification of breast abnormalities in digital mammography with a deep convolutional neural network

Alexey Ruchay,Konstantin Dorofeev,Vitaly Kober,Andrew G Tescher,Touradj Ebrahimi

doi:10.1117/12.2567252

Classification of breast abnormalities in digital mammography with a deep convolutional neural network

Alexey Ruchay, Konstantin Dorofeev + Show 3 more

https://doi.org/10.1117/12.2567252

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Publication Date: Aug 21, 2020

Affiliation: Chelyabinsk State University, South Ural State University, Federal Scientific Center for Biological Systems and Agricultural Technologies of the Russian Academy of Sciences, Center for Scientific Research and Higher Education at Ensenada

#Classification Of Breast Abnormalities #Classification Of Abnormalities + Show 8 more

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Abstract

A novel algorithm for analysis and classification of breast abnormalities in digital mammography based on a deep convolutional neural network is proposed. Simplified neural network architectures such as MobileNetV2, InceptionResNetV2, Xception, and ResNetV2 are intensively studied for this task. In order to improve the accuracy of detection and classification of breast abnormalities on real data an efficient training algorithm based on augmentation technique is suggested. The performance of the proposed algorithm for analysis and classification of breast abnormalities on real data is discussed and compared to that of the state-of-the-art algorithms.

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