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.
|Number of pages||11|
|Publication status||Published - 2006|
|Event||The 2nd International Conference on Natural Computation and the 3rd International Conference on Fuzzy systems and Knowledge Discovery : ICNC 2006 and FSKD 2006 - Xi'an, China|
Duration: 24 Sept 2006 → 28 Sept 2006
|Conference||The 2nd International Conference on Natural Computation and the 3rd International Conference on Fuzzy systems and Knowledge Discovery|
|Period||24/09/06 → 28/09/06|