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

Document Type: Research Paper

Authors

1 Reza Boostani, Ali Reza Kazemi and Serajodin Katebi, Vision and Image Processing Laboratory, School of Electrical and Computer Engineering, Shiraz University, Shiraz, Iran

2 Board of Science, Azad Universitiy Branch of Jahrom, Iran

Abstract

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.

Keywords


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