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

Alexey Ruchay; Konstantin Dorofeev; Vitaly Kober

doi:10.1117/12.2567252

21 August 2020 Classification of breast abnormalities in digital mammography with a deep convolutional neural network

Alexey Ruchay, Konstantin Dorofeev, Vitaly Kober

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Proceedings Volume 11510, Applications of Digital Image Processing XLIII; 115102D (2020) https://doi.org/10.1117/12.2567252
Event: SPIE Optical Engineering + Applications, 2020, Online Only

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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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Alexey Ruchay, Konstantin Dorofeev, and Vitaly Kober "Classification of breast abnormalities in digital mammography with a deep convolutional neural network", Proc. SPIE 11510, Applications of Digital Image Processing XLIII, 115102D (21 August 2020); https://doi.org/10.1117/12.2567252

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