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|Title:||Hybrid classification of pulmonary nodules|
|Citation:||Computational Intelligence and Intelligent Systems: 4th International Symposium on Intelligence Computation and Applications, ISICA 2009, Huangshi, China, October 23-25 2009 / Z. Cai, Z. Li, Z. Kang and Y. Liu (eds.): pp.472-481|
|Series/Report no.:||Communications in Computer and Information Science ; v. 51|
|Conference Name:||ISICA (23 Oct 2009 - 25 Oct 2009 : Communications in Computer and Information Science)|
|S. L. A. Lee, A. Z. Kouzani and E. J. Hu|
|Abstract:||Automated classification of lung nodules is challenging because of the variation in shape and size of lung nodules, as well as their associated differences in their images. Ensemble based learners have demonstrated the potentialof good performance. Random forests are employed for pulmonary nodule classification where each tree in the forest produces a classification decision, and an integrated output is calculated. A classification aided by clustering approach is proposed to improve the lung nodule classification performance. Three experiments are performed using the LIDC lung image database of 32 cases. The classification performance and execution times are presented and discussed.|
|Keywords:||nodule; detection; lung images; classification; classification aided by clustering; ensemble learning; random forest|
|Rights:||© Springer-Verlag Berlin Heidelberg 2009|
|Appears in Collections:||Mechanical Engineering publications|
Environment Institute publications
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