%0 Journal Article
%T Classification of breast abnormalities using a deep convolutional neural network and transfer learning
%A AN Ruchai
%A VI Kober
%A KA Dorofeev
%A VN Karnaukhov
%A Mikhail Mozerov
%J Journal of Communications Technology and Electronics
%D 2021
%V 66
%N 6
%F AN Ruchai2021
%O LAMP;
%O exported from refbase (http://refbase.cvc.uab.es/show.php?record=3680), last updated on Mon, 24 Oct 2022 12:23:33 +0200
%X 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.
%U https://link.springer.com/article/10.1134/S1064226921060206
%U http://dx.doi.org/10.1134/S1064226921060206
%P 778–783