Clinical X-Ray Image Based Tooth Decay Diagnosis using SVM

Automatic tooth decay diagnosis achieved the satisfying results on the extracted tooth diagnosis by rule-based system and Artificial Neural Network (ANN). This paper, focusing on clinical tooth decay detection, introduces a Support Vector Machine (SVM) based diagnosis method. For comparison, an additional back propagation neural network (BPNN) tooth decay diagnosis experiment is reported. Comparative results indicate that SVM based method gives better performance than the one BPNN based.

[1]  Corinna Cortes,et al.  Support-Vector Networks , 1995, Machine Learning.

[2]  Bernhard E. Boser,et al.  A training algorithm for optimal margin classifiers , 1992, COLT '92.

[3]  Yang Yu,et al.  [Establishment and evaluation of a computer-based software system for detection of initial approximal caries]. , 2006, Zhonghua kou qiang yi xue za zhi = Zhonghua kouqiang yixue zazhi = Chinese journal of stomatology.

[4]  Chih-Jen Lin,et al.  A Practical Guide to Support Vector Classication , 2008 .

[5]  Zhang Nan,et al.  Review of pattern recognition based on support vector machine , 2006 .

[6]  Yun Li,et al.  Tooth Decay Diagnosis using Back Propagation Neural Network , 2006, 2006 International Conference on Machine Learning and Cybernetics.