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, doi: 10.1109/ICSMC.2009.5346753. Attached Files Name Description MIMEType Size Downloads kouzani-pulmonarynoduleclassification-2009.pdf Published version application/pdf 400.09KB 106 Title Pulmonary nodule classification aided by clustering Author(s) Lee, Shu Ling Alycia Kouzani, Abbas orcid.org/0000-0002-6292-1214 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 Total pages 6 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 DOI 10.1109/ICSMC.2009.5346753 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 Free to Read? Yes Persistent URL http://hdl.handle.net/10536/DRO/DU:30029306 Document type: Conference Paper Collections: Faculty of Science, Engineering and Built Environment School of Engineering Open Access Collection Related Links Link Description Connect to published version (restricted access) Go to link with your DU access privileges     Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved. 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. Versions Version Filter Type Wed, 16 Jun 2010, 18:12:18 EST Wed, 16 Jun 2010, 18:13:38 EST Wed, 16 Jun 2010, 18:21:49 EST Thu, 17 Jun 2010, 12:53:20 EST Thu, 17 Jun 2010, 12:55:51 EST Thu, 17 Jun 2010, 12:58:18 EST Mon, 20 Feb 2012, 13:06:43 EST Tue, 21 Feb 2012, 07:23:32 EST Fri, 16 Mar 2012, 18:11:08 EST Mon, 23 Sep 2013, 11:43:47 EST Fri, 22 Aug 2014, 14:45:18 EST Fri, 06 Feb 2015, 10:37:19 EST Tue, 06 Sep 2016, 15:41:22 EST Tue, 05 Dec 2017, 09:16:26 EST Fri, 07 Sep 2018, 15:00:25 EST Filtered Full Citation counts:   Cited 7 times in TR Web of Science Cited 4 times in Scopus Search Google Scholar Access Statistics: 935 Abstract Views, 112 File Downloads  -  Detailed Statistics Created: Wed, 16 Jun 2010, 18:12:06 EST by Sandra Dunoon