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

Abstract

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.

Deep learning convolutional neural network algorithms for the early detection and diagnosis of dental caries on periapical radiographs: A systematic review

“…The term deep learning convolutional neural network or deep neural network (DNN) refers to the application of an artificial neural network (ANN) with multiple layers to analyse visual imagery, assign importance (learnable weights and biases) to various aspects of the image, and distinguish one characteristic from another. 1 8 15 16 A DNN can perform pattern recognition of images without human intervention. Once the program is established, raw data is processed via non-linear activation functions from shallow to deeper layers; as the layers get deeper, the raw data is translated into characteristics matching the representation of raw data.…”

Applications of artificial intelligence in dentistry: A comprehensive review

“…Comparing the methods used in the study, SVMs showed the best performance for the detection of root caries, with an accuracy of 97.1%, precision of 95.1%, sensitivity of 99.6%, and specificity of 93.3%. Other approached have been proposed in literature by using features extracted from photographic color images 121 and x-ray images, 122 achieving similar results.…”

“…105 Color matching 120 Radiography, [110][111][112]115 CBCT Images, 105,116 clinical/other types of data, [117][118][119][120] other image formats 113,114 ML techniques Disease identification Dental caries, 13,121,122 periodontal disease, [123][124][125][126][127] oral cancer, [128][129][130][131][132][133][134][135] dental pain, 136 oral malodour, 137 oral clefts detection. 138 Oral disease prevention 139 Radiography, 122 other image formats, 121,128,129 clinical/biological data 13,[123][124][125][126][127][130][131][132][133]…”

Automated Caries Detection using a 3D Intraoral Scanner. An in Vivo Validation Study

“…The use of 3D intraoral scanners (IOS) and corresponding software for oral disease detection and monitoring has proven potential [1][2][3][4][5] . There is increasing development in this area, both by companies that produce medical devices, and by researchers seeking improved devices and software that can support automated detection and objective monitoring of oral diseases such as caries, tooth wear and periodontal diseases either in a clinical setup or remotely [1][2][3][4][5][6][7][8][9][10][11][12] .…”