kouzani-arandomforest-2008.pdf (370.27 kB)
A random forest for lung nodule identification
conference contribution
posted on 2008-01-01, 00:00 authored by S Lee, Abbas KouzaniAbbas Kouzani, Eric HuA method is presented for identification of lung nodules. It includes three stages: image acquisition, background removal, and nodule detection. The first stage improves image quality. The second stage extracts long lobe regions. The third stage detects lung nodules. The method is based on the random forest learner. Training set contains nodule, non-nodule, and false-positive patterns. Test set contains randomly selected images. The developed method is compared against the support vector machine. True-positives of 100% and 85.9%, and false-positives of 1.27 and 1.33 per image were achieved by the developed method and the support vector machine, respectively.
History
Event
IEEE Region 10 Conference (2008 : Hyderabad, India)Pagination
1 - 5Publisher
IEEELocation
Hyderabad, IndiaPlace of publication
Piscataway, N.J.Start date
2008-11-18End date
2008-11-21ISBN-13
9781424424085Language
engPublication classification
E1 Full written paper - refereedCopyright notice
2008, IEEETitle of proceedings
TENCON 2008 : IEEE Region 10 ConferenceUsage metrics
Categories
No categories selectedKeywords
Licence
Exports
RefWorks
BibTeX
Ref. manager
Endnote
DataCite
NLM
DC