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Type: Conference paper
Title: Hybrid classification of pulmonary nodules
Author: Lee, S.
Kouzani, A.
Hu, E.
Citation: Computational Intelligence and Intelligent Systems: 4th International Symposium on Intelligence Computation and Applications, ISICA 2009, Huangshi, China, October 23-25 2009 / Z. Cai, Z. Li, Z. Kang and Y. Liu (eds.): pp.472-481
Publisher: SPRINGER
Publisher Place: Germany
Issue Date: 2009
Series/Report no.: Communications in Computer and Information Science ; v. 51
ISBN: 9783642049613
ISSN: 1865-0929
Conference Name: ISICA (23 Oct 2009 - 25 Oct 2009 : Communications in Computer and Information Science)
Editor: Cai, Z.H.
Li, Z.H.
Kang, Z.
Liu, Y.
Statement of
S. L. A. Lee, A. Z. Kouzani and E. J. Hu
Abstract: 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.
Keywords: nodule
lung images
classification aided by clustering
ensemble learning
random forest
Rights: © Springer-Verlag Berlin Heidelberg 2009
DOI: 10.1007/978-3-642-04962-0_54
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Appears in Collections:Aurora harvest 5
Environment Institute publications
Mechanical Engineering publications

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