Proceedings of the 2018 3rd International Conference on Automation, Mechanical Control and Computational Engineering (AMCCE 2018)

Mammogram classification method based on GMM and GLCM-PSO-PNN

Authors
Xiaojian Zhang, Chengjian Wei, Xili Wan
Corresponding Author
Xiaojian Zhang
Available Online May 2018.
DOI
https://doi.org/10.2991/amcce-18.2018.44How to use a DOI?
Keywords
mammogram; gauss mixture model; probabilistic neural network; gray level co-occurrence matrix; particle swarm optimization
Abstract
Facing the condition that the inefficient training of traditional classifiers in the classification process of mammography, a classification method is proposed combining image processing and supervised learning. Firstly, the improved adaptive median filter enhances the image contrast. Then, according to the result of breast segmentation based on Gauss Mixture Model (GMM), this paper proposed a classification model based on Probabilistic Neural Network optimized (PNN) optimized by Gray Level Co-occurrence Matrix (GLCM) and Particle Swarm Optimization (PSO). The eigenvector extracted from the GLCM can be used as input to simplify the network structure. The smoothing factor optimized by PSO used to train the network can improve accuracy. The results in public mammographic patches database demonstrate that the model can classify the types of mammography effectively and perform better than the previous methods.
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Proceedings
2018 3rd International Conference on Automation, Mechanical Control and Computational Engineering (AMCCE 2018)
Part of series
Advances in Engineering Research
Publication Date
May 2018
ISBN
978-94-6252-508-5
ISSN
2352-5401
DOI
https://doi.org/10.2991/amcce-18.2018.44How to use a DOI?
Open Access
This is an open access article distributed under the CC BY-NC license.

Cite this article

TY  - CONF
AU  - Xiaojian Zhang
AU  - Chengjian Wei
AU  - Xili Wan
PY  - 2018/05
DA  - 2018/05
TI  - Mammogram classification method based on GMM and GLCM-PSO-PNN
BT  - 2018 3rd International Conference on Automation, Mechanical Control and Computational Engineering (AMCCE 2018)
PB  - Atlantis Press
SN  - 2352-5401
UR  - https://doi.org/10.2991/amcce-18.2018.44
DO  - https://doi.org/10.2991/amcce-18.2018.44
ID  - Zhang2018/05
ER  -