@Article{ANRuchai2021,
author="AN Ruchai
and VI Kober
and KA Dorofeev
and VN Karnaukhov
and Mikhail Mozerov",
title="Classification of breast abnormalities using a deep convolutional neural network and transfer learning",
journal="Journal of Communications Technology and Electronics",
year="2021",
volume="66",
number="6",
pages="778--783",
abstract="A new algorithm for classification of breast pathologies in digital mammography using a convolutional neural network and transfer learning is proposed. The following pretrained neural networks were chosen: MobileNetV2, InceptionResNetV2, Xception, and ResNetV2. All mammographic images were pre-processed to improve classification reliability. Transfer training was carried out using additional data augmentation and fine-tuning. The performance of the proposed algorithm for classification of breast pathologies in terms of accuracy on real data is discussed and compared with that of state-of-the-art algorithms on the available MIAS database.",
optnote="LAMP;",
optnote="exported from refbase (http://refbase.cvc.uab.es/show.php?record=3680), last updated on Mon, 24 Oct 2022 12:23:33 +0200",
doi="10.1134/S1064226921060206",
opturl="https://link.springer.com/article/10.1134/S1064226921060206"
}