Effect of presence/absence of noise in mammogram images using fuzzy soft set based classification

Effective use of feature set and selection of a suitable classification method are significant for improving classification accuracy. However, mammogram images classification is affected by many factors such as additive gaussian noise, low contrast and artifacts. Therefore, the aim of this paper is to observe the impact of presence /absence of noise on the quality and classification accuracy of mammogram images. 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. The experimental results show the improvement of classification accuracy in the presence of noise using wavelet filter with fuzzy soft set classifier compared with the results in the absence of noise within existing classification algorithms.