Recognition of Breast Cancer Using Fuzzy Rules and Efficient Features

Breast cancer is the second leading cause of death for women all over the world. The correct diagnosis of breast cancer is one of the major problems in the medical field. Fromthe literature it has been found that different pattern recognition techniques can help them to improve in this domain. This paperpresents a novel hybrid intelligent method for detection of breast cancer tumors. The proposed method includes three mainmodules: a feature extraction module, a classifier module andan optimization module. In the feature extraction module, statistical features are proposed as the efficient characteristic of the tumors. In the classifier module adaptive neuro-fuzzy inference system (ANFIS) is proposed that is a hybridcombination of artificial neural networks (ANN) and fuzzy inference system (FIS). In ANFIS training, the vector of radius has very important role for its recognition accuracy. Therefore, in the optimization module, bees algorithm (BA) is proposed for finding optimum vector of radius. The proposed system is tested on Wisconsin Breast Cancer (WBC) database and simulation results show that the recommended system has high accuracy

Recognition of Breast Cancer Using Fuzzy Rules and Efficient Features Keywords:

ANFIS , Bees algorithm , Breast cancer , Statistical feature , WBC