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Application of decision trees for mass classification in mammography

conference contribution
posted on 06.12.2017, 00:00 by K Kumar, P Zhang, Brijesh VermaBrijesh Verma
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.

Funding

Category 1 - Australian Competitive Grants (this includes ARC, NHMRC)

History

Start Date

01/01/2006

ISBN-10

7560617352

ISBN-13

9783540459071

Location

Xi'an, China

Publisher

Springer

Place of Publication

Germany

Peer Reviewed

Yes

Open Access

No

External Author Affiliations

Bond University (Gold Coast, Qld.); Faculty of Business and Informatics;

Era Eligible

Yes

Name of Conference

International Conference on Fuzzy Systems and Knowledge Discovery;International Conference on Natural Computation