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

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-proce...

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Bibliographic Details
Published in:Journal of communications technology & electronics 2021-06, Vol.66 (6), p.778-783
Main Authors: Ruchai, A. N., Kober, V. I., Dorofeev, K. A., Karnaukhov, V. N., Mozerov, M. G.
Format: Article
Language:English
Subjects:
Abnormalities
Algorithms
Artificial neural networks
Breast cancer
Classification
Communications Engineering
Engineering
Image classification
Machine learning
Mammography
Medical imaging equipment
Networks
Neural networks
Theory and Methods of Information Processing
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Summary: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.
ISSN:1064-2269
1555-6557
DOI:10.1134/S1064226921060206