Pulmonary Nodule Classification Aided by Clustering

被引：6

作者：

Lee, S. L. A. ^{[1
]}

Kouzani, A. Z. ^{[1
]}

Nasierding, G. ^{[1
]}

Hu, E. J. ^{[2
]}

机构：

[1] Deakin Univ, Sch Engn, Waum Ponds, Vic 3217, Australia

[2] Univ Adelaide, Sch Mech Engn, North Terrace, Adelaide, SA 5005, Australia

来源：

2009 IEEE INTERNATIONAL CONFERENCE ON SYSTEMS, MAN AND CYBERNETICS (SMC 2009), VOLS 1-9 | 2009年

关键词：

classification aided by clustering; nodule; detection; LUNG NODULES; CT; SEGMENTATION; IMAGES;

D O I：

10.1109/ICSMC.2009.5346753

中图分类号：

TP3 [计算技术、计算机技术];

学科分类号：

0812 ;

摘要：

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.

引用

页码：906 / +

页数：2

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