Seyedeh Somayeh Naghibi - Electrical and Computer Eng. Dept KNT University of Technology Tehran, Iran
Mohammad Teshnehlab - Electrical and Computer Eng. Dept KNT University of Technology Tehran, Iran
Mahdi Aliyari Shoorehdeli - Electrical and Computer Eng. Dept KNT University of Technology Tehran, Iran- Advanced Process Automation andControl (APAC)

This paper presents a new approach for breast cancer detection based on Hierarchical Fuzzy Neural Network(HFNN). Generally in formal fuzzy neural networks (FNN), increasing the number of inputs, causes exponential growth in the number of parameters of the FNN system. This phenomenon named as curse of dimensionality . An approach to deal with this problem is to use the hierarchical fuzzy neural network. A HFNN consists of hierarchically connected low-dimensional fuzzy neural networks. HFNN can use less rules to model nonlinear system. This method is applied to the Wisconsin Breast Cancer Database (WBCD) to classify breast cancer into two groups: benign and malignant lesions. The performance of HFNN is then compared with FNN by using the same breast cancer dataset.

Breast Cancer; Hierarchical Fuzzy Neural Network (HFNN); fuzzy neural network (FNN); Curse of Dimensionality