Hybrid classification of pulmonary nodules

Lee, S. L. A., Kouzani, A. Z. and Hu, E. J. 2009, Hybrid classification of pulmonary nodules. In Cai, Zhihua, Li, Zhenhua, Kang, Zhuo and Liu, Yong (ed), , Springer, Berlin, Germany, pp.472-481, doi: 10.1007/978-3-642-04962-0.

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Title Hybrid classification of pulmonary nodules
Author(s) Lee, S. L. A.
Kouzani, A. Z.ORCID iD for Kouzani, A. Z. orcid.org/0000-0002-6292-1214
Hu, E. J.
Editor(s) Cai, Zhihua
Li, Zhenhua
Kang, Zhuo
Liu, Yong
Publication date 2009
Series Communications in computer and information science ; volume 51
Chapter number 54
Total chapters 54
Start page 472
End page 481
Total pages 10
Publisher Springer
Place of Publication Berlin, Germany
Keyword(s) nodule
lung images
classification aided by clustering
ensemble learning
random forest
Summary Automated classification of lung nodules is challenging because of the variation in shape and size of lung nodules, as well as their associated differences in their images. Ensemble based learners have demonstrated the potentialof good performance. Random forests are employed for pulmonary nodule classification where each tree in the forest produces a classification decision, and an integrated output is calculated. A classification aided by clustering approach is proposed to improve the lung nodule classification performance. Three experiments are performed using the LIDC lung image database of 32 cases. The classification performance and execution times are presented and discussed.
ISBN 9783642049613
ISSN 1865-0929
Language eng
DOI 10.1007/978-3-642-04962-0
Field of Research 080109 Pattern Recognition and Data Mining
080110 Simulation and Modelling
090399 Biomedical Engineering not elsewhere classified
Socio Economic Objective 920203 Diagnostic Methods
HERDC Research category B1 Book chapter
Copyright notice ©2009, Springer-Verlag
Persistent URL http://hdl.handle.net/10536/DRO/DU:30022919

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Created: Fri, 05 Feb 2010, 10:38:45 EST by Abbas Kouzani

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