Abstract: Over the past two decades, diagnosis of tooth caries or cavities is considered as one of the emerging research topics. So far, a number of methods are introduced to diagnose the tooth decaying, tooth demineralization and re-mineralization as well. However, the sophistication against the tooth decaying diagnosis arises when the environs are relatively complex. With all this in mind, this paper introduces the caries diagnosing model. Here, the feature extraction is based on Multilinear Principal Component Analysis (MPCA). Further, the classification is done by utilizing renowned classifier named Neural Network (NN). The proposed model is compared with other conventional methods such as the Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA), Auto Correlation-NN (AC-NN), Gray-Level Co-Occurrence Matrix (GLCM AC-Support Vector Machine (SVM)), and Independent Component Analysis (ICA), and the performance of the approach is analyzed in terms of measures such as Accuracy, Sensitivity, Specificity, Precision, False Positive Rate (FPR), False Negative Rate (FNR), Negative Predictive Value (NPV), False Discovery Rate (FDR), F1 Score and Mathews correlation coefficient (MCC). Through quantitative analysis, the proposed model proves its efficiency over the conventional methods in detecting caries.
