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

DOI

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.

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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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  - Proceedings of the 2018 3rd International Conference on Automation, Mechanical Control and Computational Engineering (AMCCE 2018)
PB  - Atlantis Press
SP  - 251
EP  - 258
SN  - 2352-5401
UR  - https://doi.org/10.2991/amcce-18.2018.44
DO  - 10.2991/amcce-18.2018.44
ID  - Zhang2018/05
ER  -