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Pulmonary nodule classification aided by clustering
conference contribution
posted on 2009-01-01, 00:00 authored by S Lee, Abbas KouzaniAbbas Kouzani, Gulisong NasierdingGulisong NasierdingLung 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.
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Event
IEEE International Conference on Systems, Man, and Cybernetics (2009 : San Antonio, Texas)Pagination
906 - 911Publisher
IEEELocation
San Antonio, TexasPlace of publication
Piscataway, N. J.Publisher DOI
Start date
2009-10-11End date
2009-10-14ISSN
1062-922XISBN-13
9781424427949Language
engPublication classification
E1 Full written paper - refereed; E Conference publicationCopyright notice
2009, IEEETitle of proceedings
SMC 2009 : Proceedings of the IEEE International Conference on Systems, Man and CyberneticsUsage metrics
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