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Type: Conference paper
Title: Lung nodules detection by ensemble classification
Author: Kouzani, A.
Lee, S.
Hu, E.
Citation: IEEE International Conference on Systems, Man and Cybernetics, 2008: pp.324-329
Publisher: IEEE
Publisher Place: Online
Issue Date: 2008
Series/Report no.: IEEE International Conference on Systems Man and Cybernetics Conference Proceedings
ISBN: 9781424423835
ISSN: 1062-922X
Conference Name: IEEE International Conference on Systems, Man and Cybernetics (2008 : Singapore)
Statement of
A. Z. Kouzani, S. L. A. Lee, and E. J. Hu
Abstract: A method is presented that achieves lung nodule detection by classification of nodule and non-nodule patterns. It is based on random forests which are ensemble learners that grow classification trees. Each tree produces a classification decision, and an integrated output is calculated. The performance of the developed method is compared against that of the support vector machine and the decision tree methods. Three experiments are performed using lung scans of 32 patients including thousands of images within which nodule locations are marked by expert radiologists. The classification errors and execution times are presented and discussed. The lowest classification error (2.4%) has been produced by the developed method.
Keywords: lung images
ensemble learning
random forest.
DOI: 10.1109/ICSMC.2008.4811296
Appears in Collections:Aurora harvest 5
Environment Institute publications
Mechanical Engineering conference papers

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