Proceedings of the 2018 3rd International Conference on Control, Automation and Artificial Intelligence (CAAI 2018)

Mammography Classification Based on Convolutional Neural Network

Authors
Changjiang Zhang, Huanhuan Nie
Corresponding Author
Changjiang Zhang
Available Online August 2018.
DOI
https://doi.org/10.2991/caai-18.2018.35How to use a DOI?
Keywords
mammography; classification; convolutional neural network; DDSM
Abstract

Limited by various conditions, the features of mammography images are difficult to extract, so it is hard to classify them. The paper proposed a method based on deep learning method to classify benign and malignant mammography images. The convolutional neural network concludes four convolution layers, four pool layers, and two full-connection layers, and a Softmax layer. The paper designed a new network architecture to improve the traditional one. As a result, we have done an experiment on the DDSM database. Compared with other classification methods, it shows that the method proposed in this paper is more effective than other methods.

Copyright
© 2018, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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Volume Title
Proceedings of the 2018 3rd International Conference on Control, Automation and Artificial Intelligence (CAAI 2018)
Series
Atlantis Highlights in Intelligent Systems
Publication Date
August 2018
ISBN
978-94-6252-595-5
ISSN
2589-4919
DOI
https://doi.org/10.2991/caai-18.2018.35How to use a DOI?
Copyright
© 2018, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Cite this article

TY  - CONF
AU  - Changjiang Zhang
AU  - Huanhuan Nie
PY  - 2018/08
DA  - 2018/08
TI  - Mammography Classification Based on Convolutional Neural Network
BT  - Proceedings of the 2018 3rd International Conference on Control, Automation and Artificial Intelligence (CAAI 2018)
PB  - Atlantis Press
SP  - 151
EP  - 154
SN  - 2589-4919
UR  - https://doi.org/10.2991/caai-18.2018.35
DO  - https://doi.org/10.2991/caai-18.2018.35
ID  - Zhang2018/08
ER  -