An evolutionary neuro-fuzzy approach to breast cancer diagnosis

The important role that mammography is playing in breast cancer detection can be attributed largely to the technical improvements and dedication of radiologists to breast imaging. A lot of work is being done to ensure that these diagnosing steps are becoming smoother, faster and more accurate in classifying whether the abnormalities seen in mammogram images are benign or malignant. In this paper, an evolutionary approach for design of TSK-type fuzzy model (TFM) is proposed to solve the breast cancer diagnosis problem. In the proposed method, both the number of fuzzy rules and adjustable parameters in the TFM are designed concurrently combining the compact genetic algorithm (CGA) and the steady-state genetic algorithm (SSGA). The computational experiments show that the presented approach can obtain better generalization than some existing methods reported recently in the literature using the widely accepted Wisconsin breast cancer diagnosis (WBCD) database.