A Support Vector Based Fuzzy Neural Network Approach for Mass Classification in Mammography

In this paper, a new approach for mass classification in digital mammograms based on contourlet texture features and support-vector-based fuzzy neural network (SVFNN) classifier is presented. The SVFNN combines the superior classification power of support vector machine (SVM) in high dimensional dat...

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Bibliographic Details
Main Authors: Moayedi, F., Boostani, R., Azimifar, Z., Katebi, S.
Format: Conference Proceeding
Language:English
Subjects:
contourlet
Data mining
Feature extraction
Fuzzy neural networks
Fuzzy reasoning
Machine learning
Mammography
Mass
Neural networks
Support Vector Based Fuzzy Neural Network (SVFNN)
Support vector machine classification
Support vector machines
Uncertainty
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Description
Summary:In this paper, a new approach for mass classification in digital mammograms based on contourlet texture features and support-vector-based fuzzy neural network (SVFNN) classifier is presented. The SVFNN combines the superior classification power of support vector machine (SVM) in high dimensional data spaces, the efficient human-like reasoning of fuzzy in handling uncertainty information, and learning property of neural networks. Each mammogram is segmented to regions of interest and features are extracted in frequency domain by contourlet coefficients. One of the main contribution of this research is taking benefit from the superiority of the contourlet compared to the multi-scale techniques and use SVFNN for mass classification. MIAS1 data set is used to evaluate the proposed method. Experimental results demonstrate that the method presented is a promising method for mass classification in mammography.
ISSN:1546-1874
2165-3577
DOI:10.1109/ICDSP.2007.4288563