Algorithmic analysis for dental caries detection using an adaptive neural network architecture

Objectives: AI techniques have lifelong impact in biomedics and widely accepted outcomes. The sole objective of the study is to evaluate accurate detection of caries using feature extraction and classification of the dental images along with amalgamation Adaptive Dragonfly algorithm (DA) algorithm and Neural Network (NN) classifier. Materials and methods: Here proposed caries detection model is designed for detecting the tooth cavities in an accurate manner. This methodology has two main phases; feature extraction and classification. In all total 120 images database is split into three sets, randomly for evaluating the performance. Further, this categorization of the test cases aids in ensuring the enhancement of the performance. In each of the test cases, there are 40 caries images the investigation in the performance of the proposed caries detection model was done in terms of accuracy, sensitivity, specificity, and precision, FPR, FNR, NPV, FDR, F1Score and MCC. Results: Here MPCA with Nonlinear Programming and Adaptive DA, the proposed model is termed as MNP-ADA. The performance of the proposed MPCA-ADA model is evaluated by comparing it over the other existing feature extraction models. MPCA-ADA over the conventional classifier models like PCA-ADA, LDA-ADA and ICA-ADA in terms of performance parameters and MCC for all the test types and have superior results than the existing ones. Conclusion: The research work emphasizes the prospective efficacy of IP and NN algorithms for the detection and diagnosis of dental caries. A novel and improved model shows substantially worthy performance in distinguishing dental caries using image processing techniques.