Open AccessIntelligent system with dragonfly optimisation for caries detection
Author(s) -
Patil Shashikant,
Kulkarni Vaishali,
Bhise Archana
Publication year - 2019
Publication title -
iet image processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.401
H-Index - 45
eISSN - 1751-9667
pISSN - 1751-9659
DOI - 10.1049/iet-ipr.2018.5442
Subject(s) - classifier (uml) , artificial intelligence , principal component analysis , computer science , pattern recognition (psychology) , feature extraction , false positive rate , artificial neural network , correlation coefficient , machine learning
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.