Abstract
Age estimation is of prime significance in forensic science and clinical dentistry. Age estimation based on teeth improvement is one solid approach. Numerous radiographic strategies are proposed on the southern populace for evaluating Dental Age (DA), and a comparative appraisal was observed to be insufficient in Indian populace. Henceforth, this investigation goes for detailing a classification model for DA estimation in Indian kid’s populace utilizing Demirjian’s technique. In this exploration, a Fuzzy Neural Network with Teaching Learning - Based Optimization (FNN-TLBO) is proposed for classification of DA. At first, the OPG input image is preprocessed for diminishing noise and smoothing the image by utilizing Anisotropic Diffusion Filter (ADF). Thusly, the whole teeth from teeth picture are portioned utilizing Active Contour Model (ACM) with Analytic Hierarchy Process (AHP) optimization and after that morphological post handling has been connected on the sectioned outcome to advance the order precision. Next, specific highlights are removed, for example, GLCM, Haralick features, Haufsdroff distance, crown and root, tooth density, size, Geometric features such as roughness, concavity, convexity, area and perimeter to upgrade the expectation exactness. Finally, the age has been classified with FNN-TLBO. In this FNN, TLBO is utilized to take care of the system training issue. Recreation results shows that the expected FNN-TLBO procures better execution with deference than accuracy rate of 89%, specificity rate of 89.12%, precision rate of 64.152%, recall rate of 92% and F-measure rate of 71.12% compared than exist algorithms like Modified Extreme Learning Machine with Sparse Representation Classification (MELM-SRC), Radial Basis Function Network (RBFN), Demirjian and Adaptive Neuro Fuzzy Inference System (ANFIS) schemes.
Published Version
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