Pulmonary nodule classification aided by clustering
Date
2009
Authors
Lee, S.L.A.
Kouzani, A.Z.
Nasierding, G.
Hu, E.J.
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Conference paper
Citation
Conference proceedings / IEEE International Conference on Systems, Man, and Cybernetics. IEEE International Conference on Systems, Man, and Cybernetics, 2009, pp.906-911
Statement of Responsibility
S.L.A. Lee, A.Z. Kouzani, and G. Nasierding, E.J. Hu
Conference Name
2009 IEEE International Conference on Systems, Man and Cybernetics (SMC 2009) (11 Oct 2009 - 14 Oct 2009 : San Antonio, TX)
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.
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©2009 IEEE