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Lung nodules detection by ensemble classification

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conference contribution
posted on 2008-01-01, 00:00 authored by Abbas KouzaniAbbas Kouzani, S Lee, Eric Hu
A 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 - 329

Publisher

IEEE

Location

Singapore

Place of publication

Piscataway, N.J.

Start date

2008-10-12

End date

2008-10-15

ISBN-13

9781424423842

Language

eng

Publication classification

E1 Full written paper - refereed

Copyright notice

2008, IEEE

Editor/Contributor(s)

IE

Title of proceedings

SMC 2008 : Proceedings of 2008 IEEE International Conference on Systems, Man and Cybernetics