FEATURE EXTRACTION BASED WAVELET TRANSFORM IN BREAST CANCER DIAGNOSIS USING FUZZY AND NON-FUZZY CLASSIFICATION

Abstract

This study helps to provide a second eye to the expert radiologists for the classification of manually extracted breast masses taken from 60 digital mammıgrams. These mammograms have been acquired from Istanbul University Faculty of Medicine Hospital and have 78 masses. The diagnosis is implemented with pre-processing by using feature extraction based Fast Wavelet Transform (FWT). Afterwards Adaptive Neuro-Fuzzy Inference System (ANFIS) based fuzzy subtractive clustering and Support Vector Machines (SVM) methods are used for the classification. It is a comparative study which uses these methods respectively. According to the results of the study, ANFIS based subtractive clustering produces ??% while SVM produces ??% accuracy in malignant-benign classification. The results demonstrate that the developed system could help the radiologists for a true diagnosis and decrease the number of the missing cancerous regions or unnecessary biopsies.