TY - GEN
T1 - Hybrid SVM - Random Forest classication system for oral cancer screening using LIF spectra
AU - Singh, Rahul Kumar
AU - Naik, Sarif Kumar
AU - Gupta, Lalit
AU - Balakrishnan, Srinivasan
AU - Santhosh, C.
AU - Pai, Keerthilatha M.
PY - 2008/12/1
Y1 - 2008/12/1
N2 - In this paper, a system for oral cancer screening using Laser Induced Fluorescence(LIF) has been developed. A hybrid approach of classication using Support Vector Machine (SVM) and Random Forest (RF) classier's is proposed. Performance of the classier is evaluated using several features types such as Wavelet, DFT, LDFT, ILDFT, DCT, LDCT and Slopes features. The most discriminating features are selected using Recursive Feature Elimination(RFE). Analysis of the problem of subset selection from SVM-RFE ranked list is also performed. The hybrid approach has been compared with stand-alone SVM, SVM-RFE and RF classiers. The proposed technique improves the performance of the classication system signicantly. The novelty of the approach lies in the way the most signicant features are exstracted in separate modules to arrive at a decision and how the decision are then fused in an intelligent fashion to arrive at a final classication.
AB - In this paper, a system for oral cancer screening using Laser Induced Fluorescence(LIF) has been developed. A hybrid approach of classication using Support Vector Machine (SVM) and Random Forest (RF) classier's is proposed. Performance of the classier is evaluated using several features types such as Wavelet, DFT, LDFT, ILDFT, DCT, LDCT and Slopes features. The most discriminating features are selected using Recursive Feature Elimination(RFE). Analysis of the problem of subset selection from SVM-RFE ranked list is also performed. The hybrid approach has been compared with stand-alone SVM, SVM-RFE and RF classiers. The proposed technique improves the performance of the classication system signicantly. The novelty of the approach lies in the way the most signicant features are exstracted in separate modules to arrive at a decision and how the decision are then fused in an intelligent fashion to arrive at a final classication.
UR - http://www.scopus.com/inward/record.url?scp=77957966709&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=77957966709&partnerID=8YFLogxK
U2 - 10.1109/icpr.2008.4761357
DO - 10.1109/icpr.2008.4761357
M3 - Conference contribution
AN - SCOPUS:77957966709
SN - 9781424421756
T3 - Proceedings - International Conference on Pattern Recognition
BT - 2008 19th International Conference on Pattern Recognition, ICPR 2008
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2008 19th International Conference on Pattern Recognition, ICPR 2008
Y2 - 8 December 2008 through 11 December 2008
ER -
