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Lung nodules detection by ensemble classification

Kouzani, A., Lee, S. L. A. and Hu, E. J. 2008, Lung nodules detection by ensemble classification, in SMC 2008 : Proceedings of 2008 IEEE International Conference on Systems, Man and Cybernetics, IEEE, Piscataway, N.J., pp. 324-329.

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Title Lung nodules detection by ensemble classification
Author(s) Kouzani, A.
Lee, S. L. A.
Hu, E. J.
Conference name IEEE International Conference on Systems, Man and Cybernetics (2008 : Singapore)
Conference location Singapore
Conference dates 12-15 October 2008
Title of proceedings SMC 2008 : Proceedings of 2008 IEEE International Conference on Systems, Man and Cybernetics
Editor(s) IE
Publication date 2008
Conference series International Conference on Systems, Man and Cybernetics
Start page 324
End page 329
Publisher IEEE
Place of publication Piscataway, N.J.
Keyword(s) lung images
nodule
detection
classification
ensemble learning
random forest
Summary 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.
ISBN 9781424423842
Language eng
Field of Research 080109 Pattern Recognition and Data Mining
Socio Economic Objective 920203 Diagnostic Methods
HERDC Research category E1 Full written paper - refereed
Copyright notice ©2008, IEEE
Persistent URL http://hdl.handle.net/10536/DRO/DU:30018305

Document type: Conference Paper
Collections: School of Engineering and Information Technology
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Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.