Comparative performance analysis of Naive Bayes and SVM classifier for oral X-ray images

This paper presents the development of an automatic system for the classification of tooth wear disease diagnosis. Abnormal detection, disease detection and classification of oral images are substantial in the clinical research. Automatic diagnosis of the oral disease helps the medical practitioner to make decisions easily about the diagnosis process. The diagnosis models can be analyzed with the multiclass classification approach. The multiclass Naive Bayes and Support Vector Machine (SVM) classifier have been used for diagnosing the oral diseases and also their performance has been evaluated and compared. The experimental results are measured in terms of Sensitivity, Specificity, Precision, False Alarm and Accuracy which are considered as the performance parameters of classifier. Furthermore, our analysis proves that the SVM classifier achieves better result.