Classification of Breast Abnormalities Using a Deep Convolutional Neural Network and Transfer Learning

被引：0
作者：
A. N. Ruchai
V. I. Kober
K. A. Dorofeev
V. N. Karnaukhov
M. G. Mozerov
机构：
[1] Chelyabinsk State University,
[2] Institute for Information Transmission Problems (Kharkevich Institute),undefined
[3] South Ural State University,undefined
[4] Center of Scientific Research and Higher Education,undefined
来源：
Journal of Communications Technology and Electronics | 2021年 / 66卷
关键词：
: classification abnormalities; digital mammography; deep convolutional neural network; transfer learning; data augmentation; fine-tuning;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
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.
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页码：778 / 783
页数：5
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