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Pulmonary nodule classification aided by clustering

Lee, Shu Ling Alycia, Kouzani, Abbas and Nasierding, Gulisong 2009, Pulmonary nodule classification aided by clustering, in SMC 2009 : Proceedings of the IEEE International Conference on Systems, Man and Cybernetics, IEEE, Piscataway, N. J., pp. 906-911.

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Title Pulmonary nodule classification aided by clustering
Author(s) Lee, Shu Ling Alycia
Kouzani, Abbas
Nasierding, Gulisong
Conference name IEEE International Conference on Systems, Man, and Cybernetics (2009 : San Antonio, Texas)
Conference location San Antonio, Texas
Conference dates 11-14 Oct. 2009
Title of proceedings SMC 2009 : Proceedings of the IEEE International Conference on Systems, Man and Cybernetics
Editor(s) [Unknown]
Publication date 2009
Conference series International Conference on Systems, Man and Cybernetics
Start page 906
End page 911
Publisher IEEE
Place of publication Piscataway, N. J.
Keyword(s) classification aided by clustering
nodule
detection
Summary Lung nodules can be detected through examining CT scans. An automated lung nodule classification system is presented in this paper. The system employs random forests as it base classifier. A unique architecture for classification-aided-by-clustering is presented. Four experiments are conducted to study the performance of the developed system. 5721 CT lung image slices from the LIDC database are employed in the experiments. According to the experimental results, the highest sensitivity of 97.92%, and specificty of 96.28% are achieved by the system. The results demonstrate that the system has improved the performances of its tested counterparts.
ISBN 9781424427949
ISSN 1062-922X
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
ERA Research output type E Conference publication
HERDC collection year 2009
Copyright notice ©2009, IEEE
Persistent URL http://hdl.handle.net/10536/DRO/DU:30029306

Document type: Conference Paper
Collections: School of Engineering
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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.