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Hybrid classification of pulmonary nodules
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.
History
Title of book
Computational intelligence and intelligent systemsSeries
Communications in computer and information science ; volume 51Chapter number
54Pagination
472 - 481Publisher
SpringerPlace of publication
Berlin, GermanyPublisher DOI
ISSN
1865-0929eISSN
1865-0937ISBN-13
9783642049613ISBN-10
3642049613Language
engPublication classification
B1 Book chapterCopyright notice
2009, Springer-VerlagExtent
54Editor/Contributor(s)
Z Cai, Z Li, Z Kang, Y LiuUsage metrics
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