Research of Neural Network Classifier Based on FCM and PSO for Breast Cancer Classification

Breast cancer is one of the most common tumors related to death in women in many countries. In this paper, a novel neural network classification model is developed. The proposed model uses floating centroids method and particle swarm optimization algorithm with inertia weight as optimizer to improve the performance of neural network classifier. Wisconsin breast cancer datasets in UCI Machine Learning Repository are tested with neural network classifier of the proposed method. Experimental results show that the developed model improves search convergence and performance. The accuracy of classification of benign and malignant tumors could be improved by the developed method compared with other classification techniques.