Fuzzy Soft Set based Classification for Mammogram Images

Abstract

Mammogram images classification using data mining methods review on past literature showed that these methods are relatively successful however accuracy and efficiency are still outstanding issues. Therefore, the positive reviews produced from past works on fuzzy soft set based classification have resulted in an attempt to use similarity approach on fuzzy soft set for mammogram images classification. Thus, the proposed methodology involved five steps that are data collection, images de-noising using wavelet hard and soft thresholding, region of interest (ROI) identification, feature extraction (statistical texture features), and classification. Hundred and twelve images (68 benign images and 51 malignant images) were used for experimental set ups. Experimental results show better classification accuracy in the presence/absence of noise in mammogram images.