Title: Decision tree classifiers for mass classification

Authors: R. Nithya; B. Santhi

Addresses: School of Computing, SASTRA University, Thanjavur, Tamil Nadu 613402, India ' School of Computing, SASTRA University, Thanjavur, Tamil Nadu 613402, India

Abstract: Mass detection from the mammogram is important for breast cancer diagnosis. This paper proposes the classification method for breast masses using the decision tree techniques. This paper presents the comparison result of 12 decision tree algorithms including ADTree, BFTree, DecisionStump, FT, C4.5, LADTree, LMT, NBTree, RandomForest, RandomTree, REPTree and CART. In comparison, four performance metrics were used. The aim of the study is to determine the best decision tree classifier for mass classification from BI-RADS features (mass shape, mass margin, assessment and subtlety). In the experimental studies, all these decision tree algorithms are applied on the UCI data set. Experimental results show that LADTree and LMT has a better performance than ADTree, BFTree, DecisionStump, FT, C4.5, NBTree, RandomForest, Random Tree, REPTree and CART.

Keywords: mammograms; BI-RADS; breast imaging; breast cancer diagnosis; decision tree classification; mass classification; mammography; mammographic images.

DOI: 10.1504/IJSISE.2015.067068

International Journal of Signal and Imaging Systems Engineering, 2015 Vol.8 No.1/2, pp.39 - 45

Received: 04 Feb 2013
Accepted: 10 Nov 2013
Published online: 25 Jan 2015 *

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