Pulmonary nodule classification aided by clustering

Authors:
S. L. A. Lee;A. Z. Kouzani;G. Nasierding;E. J. Hu
Affiliations:
School of Engineering, Deakin University, Waurn Ponds, VIC, Australia;School of Engineering, Deakin University, Waurn Ponds, VIC, Australia;School of Engineering, Deakin University, Waurn Ponds, VIC, Australia;School of Mechanical Engineering, Adelaide University, Adelaide, SA, Australia
Venue:
SMC'09 Proceedings of the 2009 IEEE international conference on Systems, Man and Cybernetics
Year:
2009

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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.