Breast Tumors Diagnosis Using Fuzzy Inference System and Fuzzy C-Means Clustering

Abstract

Many of the researches have been successful in the field of computer-aided diagnosis because of the important results the intelligent computing approaches have achieved in this field. In this paper the robust classification method is presented, that attempts to classify the tissue suspicion region as normal or not normal by using a Fuzzy Inference System (FIS) using the Fuzzy C-Mean (FCM) clustering for fuzzification of the Gray-Level Co-Occurrence Matrix (GLCM) feature and a match shape function for fuzzification of matrix shape, then by using (T-norm) generate 729 rules (243 rules based on normal DB case, 243 rules based on benign case, 243 rules based on malignant case), after that the best Eighteen rules are selected (best 6 rules based on normal DB case, best 6 rules based on benign DB case, best 6 rules based on malignant DB case) by using genetic algorithm, then make summation for each group if the summation of 6 rules based on normal DB is greater than other summation of two group (best 6 rules based on benign DB case and best 6 rules based on malignant DB case) that mean resulted of the classification step is normal. The model approved efficiency classification rate of 97.5% of input dataset image.