Mammogram classification method based on GMM and GLCM-PSO-PNN
Zhenjie Zhang, Chengjian Wei, Xili Wan · Proceedings of the 2018 3rd International Conference on Automation, Mechanical Control and Computational Engineering (AMCCE 2018) · 2018
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.