Random forest based lung nodule classification aided by clustering

Lee, S. L. A., Kouzani, A. Z. and Hu, E. J. 2010, Random forest based lung nodule classification aided by clustering, Computerized medical imaging and graphics, vol. 34, no. 7, pp. 535-542, doi: 10.1016/j.compmedimag.2010.03.006.

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Title Random forest based lung nodule classification aided by clustering
Author(s) Lee, S. L. A.
Kouzani, A. Z.ORCID iD for Kouzani, A. Z. orcid.org/0000-0002-6292-1214
Hu, E. J.
Journal name Computerized medical imaging and graphics
Volume number 34
Issue number 7
Start page 535
End page 542
Total pages 8
Publisher Amsterdam, The Netherlands
Place of publication Elsevier
Publication date 2010-10
ISSN 0895-6111
Keyword(s) lung images
pulmonary nodules
ensemble classification
classification aided by clustering
Summary An automated lung nodule detection system can help spot lung abnormalities in CT lung images. Lung nodule detection can be achieved using template-based, segmentation-based, and classification-based methods. The existing systems that include a classification component in their structures have demonstrated better performances than their counterparts. Ensemble learners combine decisions of multiple classifiers to form an integrated output. To improve the performance of automated lung nodule detection, an ensemble classification aided by clustering (CAC) method is proposed. The method takes advantage of the random forest algorithm and offers a structure for a hybrid random forest based lung nodule classification aided by clustering. Several experiments are carried out involving the proposed method as well as two other existing methods. The parameters of the classifiers are varied to identify the best performing classifiers. The experiments are conducted using lung scans of 32 patients including 5721 images within which nodule locations are marked by expert radiologists. Overall, the best sensitivity of 98.33% and specificity of 97.11% have been recorded for proposed system. Also, a high receiver operating characteristic (ROC) Az of 0.9786 has been achieved.
Language eng
DOI 10.1016/j.compmedimag.2010.03.006
Field of Research 080109 Pattern Recognition and Data Mining
090609 Signal Processing
080106 Image Processing
Socio Economic Objective 920203 Diagnostic Methods
HERDC Research category C1 Refereed article in a scholarly journal
ERA Research output type C Journal article
HERDC collection year 2010
Copyright notice ©2010, Elsevier
Persistent URL http://hdl.handle.net/10536/DRO/DU:30029731

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Created: Wed, 18 Aug 2010, 23:08:23 EST by Abbas Kouzani

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