Intelligent system with dragonfly optimisation for caries detection
Shashikant Malagoud Patil, Vaishali Kulkarni, Archana Bhise · IET Image Processing · 2018
Recently, tooth decay detection is considered as one of the emerging topics. Many diagnostic techniques have been successfully presented to diagnose the problems. However, the complexity in the tooth decaying diagnosis ascends when the environs are moderately difficult. Thus, this study introduces a novel caries detecting model for the accurate detection of tooth cavities. The model is divided into two phases: feature extraction and classification. Here, the feature extraction is based on multi‐linear principal component analysis (MPCA), and the classification is processed using renowned neural network (NN) classifier. The NN classifier is trained using the adaptive dragonfly algorithm (ADA) algorithm. The proposed MPCA model Non‐linear Programming with ADA (MNP‐ADA) performance is compared with other existing methods and the performance of the approach is analysed in terms of measures such as accuracy, sensitivity, specificity, precision, false positive rate, false negative rate, negative predictive value, false discovery rate, F 1 ‐score, and Mathews correlation coefficient. The performance of the proposed model is analysed in terms of feature analysis and classifier analysis by comparing other models and proves the superiority of the developed caries detection model.