SUBCLASS FUZZY-SVM CLASSIFIER AS AN EFFICIENT METHOD TO ENHANCE THE MASS DETECTION IN MAMMOGRAMS

This paper is concerned with the development of a novel classifier for automatic mass detection of mammograms, based on contourlet feature extraction in conjunction with statistical and fuzzy classifiers. In this method, mammograms are segmented into regions of interest (ROI) in order to extract features including geometrical and contourlet coefficients. The extracted features benefit from the superiority of the contourlet method to the state of the art multi-scale techniques. A genetic algorithm is applied for feature weighting with the objective of increasing classification accuracy. Although fuzzy classifiers are interpretable, the majority are order sensitive and suffer from the lack of generalization. In this study, a kernel SVM is integrated with a nerofuzzy rule-based classifier to form a support vector based fuzzy neural network (SVFNN). This classifier benefits from the superior classification power of SVM in high dimensional data spaces and also from the efficient human-like reasoning of fuzzy and neural networks in handling uncertainty information. We use the Mammographic Image Analysis Society (MIAS) standard data set and the features extracted of the digital mammograms are applied to the fuzzy-SVM classifiers to assess the performance. Our experiments resulted in 95.6%,91.52%,89.02%, 85.31% classification accuracy for the subclass FSVM, SVFNN, fuzzy rule based and kernel SVM classifiers respectively and we conclude that the subclass fuzzy-SVM is superior to the other classifiers.