kouzani-lungnodulesdetection-2008.pdf (1.09 MB)
Lung nodules detection by ensemble classification
conference contribution
posted on 2008-01-01, 00:00 authored by Abbas KouzaniAbbas Kouzani, S Lee, Eric HuA method is presented that achieves lung nodule detection by classification of nodule and non-nodule patterns. It is based on random forests which are ensemble learners that grow classification trees. Each tree produces a classification decision, and an integrated output is calculated. The performance of the developed method is compared against that of the support vector machine and the decision tree methods. Three experiments are performed using lung scans of 32 patients including thousands of images within which nodule locations are marked by expert radiologists. The classification errors and execution times are presented and discussed. The lowest classification error (2.4%) has been produced by the developed method.
History
Event
IEEE International Conference on Systems, Man and Cybernetics (2008 : Singapore)Pagination
324 - 329Publisher
IEEELocation
SingaporePlace of publication
Piscataway, N.J.Publisher DOI
Start date
2008-10-12End date
2008-10-15ISBN-13
9781424423842Language
engPublication classification
E1 Full written paper - refereedCopyright notice
2008, IEEEEditor/Contributor(s)
IETitle of proceedings
SMC 2008 : Proceedings of 2008 IEEE International Conference on Systems, Man and CyberneticsUsage metrics
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