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Pulmonary nodule classification aided by clustering
conference contributionposted on 2009-01-01, 00:00 authored by S Lee, Abbas KouzaniAbbas Kouzani, Gulisong NasierdingGulisong Nasierding
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.
EventIEEE International Conference on Systems, Man, and Cybernetics (2009 : San Antonio, Texas)
Pagination906 - 911
LocationSan Antonio, Texas
Place of publicationPiscataway, N. J.
Publication classificationE1 Full written paper - refereed; E Conference publication
Copyright notice2009, IEEE
Title of proceedingsSMC 2009 : Proceedings of the IEEE International Conference on Systems, Man and Cybernetics
CategoriesNo categories selected