Application of Decision Trees for Mass Classification in Mammography

Kuldeep Kumar, Bond University
Ping Zhang, University of Queensland
Brijesh Verma, Central Queensland University

Article comments

Kumar, Kuldeep, Zhang, Ping and Verma, Brijesh (2006) Application of Decision Trees for Mass Classification in Mammography is a conference paper presented at The 2nd International Conference on Natural Computation and the 3rd International Conference on Fuzzy Systems and Knowledge Discovery, September 2006, Xi'an, China.
A copy of this conference paper is published in the conference proceedings Advances in Natural Computation and Data Mining by Xidian University Press

2006 HERDC submission

Abstract

This paper discusses the effectiveness of using decision trees for mass classification in mammography. The decision tree algorithms implemented by CART (Classification and Regression Trees) and See5 were used for the experiments. Different costs for type I and type II misclassification were applied for the experiments. The results obtained using algorithms based on decision trees were compared with that produced by neural network which was reported giving the higher classification rate than statistical models, with higher standard deviation. It is concluded that the decision trees are very promising for the classification of breast masses in digital mammograms.