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Hybrid SVM - Random Forest classication system for oral cancer screening using LIF spectra

In this paper, a system for oral cancer screening using Laser Induced Fluorescence(LIF) has been developed. A hybrid approach of classification using Support Vector Machine (SVM) and Random Forest (RF) classifier's is proposed. Performance of the classifier 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 classifiers. The proposed technique improves the performance of the classification system significantly. The novelty of the approach lies in the way the most significant 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 classification.