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|Title:||Pulmonary nodule classification aided by clustering|
|Citation:||Proceedings, 2009 IEEE International Conference on Systems, Man and Cybernetics, October 11-14, 2009 San Antonio, Texas, USA, 2009 / pp.906-911|
|Series/Report no.:||IEEE International Conference on Systems Man and Cybernetics Conference Proceedings|
|Conference Name:||2009 IEEE International Conference on Systems, Man and Cybernetics (SMC 2009) (11 Oct 2009 - 14 Oct 2009 : San Antonio, TX)|
|S.L.A. Lee, A.Z. Kouzani, and G. Nasierding, E.J. Hu|
|Abstract:||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 its 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 specificity of 96.28% are achieved by the system. The results demonstrate that the system has improved the performances of its tested counterparts.|
|Keywords:||classification aided by clustering; nodule; detection|
|Appears in Collections:||Mechanical Engineering publications|
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