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A random forest for lung nodule identification

Lee, S. L. A., Kouzani, A. Z. and Hu, E. J. 2008, A random forest for lung nodule identification, in TENCON 2008 : IEEE Region 10 Conference, IEEE, Piscataway, N.J., pp. 1-5.

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Title A random forest for lung nodule identification
Author(s) Lee, S. L. A.
Kouzani, A. Z.
Hu, E. J.
Conference name IEEE Region 10 Conference (2008 : Hyderabad, India)
Conference location Hyderabad, India
Conference dates 18-21 November 2008
Title of proceedings TENCON 2008 : IEEE Region 10 Conference
Editor(s) [Unknown]
Publication date 2008
Conference series IEEE Region 10 Conference
Start page 1
End page 5
Publisher IEEE
Place of publication Piscataway, N.J.
Summary A method is presented for identification of lung nodules. It includes three stages: image acquisition, background removal, and nodule detection. The first stage improves image quality. The second stage extracts long lobe regions. The third stage detects lung nodules. The method is based on the random forest learner. Training set contains nodule, non-nodule, and false-positive patterns. Test set contains randomly selected images. The developed method is compared against the support vector machine. True-positives of 100% and 85.9%, and false-positives of 1.27 and 1.33 per image were achieved by the developed method and the support vector machine, respectively.
ISBN 9781424424085
Language eng
Field of Research 080106 Image Processing
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:30018296

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.