A modified machine learning classification for dental age assessment with effectual ACM-JO based segmentation. (24th March 2021) - Hemalatha, B.; Rajkumar, N.
Estimation of dental age plays a vital role in anthropology, forensics, and bio archaeology. Specific age estimation is mandatory for living and dead individuals, especially in young adolescents and children. Diverse detection of dental age schemes is calculated though they have certain limitations, such as reliability and prediction accuracy. To resolve this, a modified extreme learning machine with sparse representation classification (MELM-SRC) is used with dental image in this work. Initially, input image is preprocessed for reducing noise and smoothing in image using an anisotropic diffusion filter (ADF). Subsequently, teeth images are segmented using active contour model (ACM) with Jaya optimisation (JO) and then morphological post processing has been applied on segmented result to progress classification accuracy. Next, certain features are extracted such as area, perimeter, solidity, diameter, major and minor axis length, and filled area to enhance prediction accuracy. Lastly, age has been classified with MELM-SRC. In this MELM, effectual features are classified using SRC to increase age classification accuracy. Simulation outcomes show anticipated MELM-SRC acquires superior performance than Demirjian method for dental age assessment and also other existing classification schemes such as radial basis function network (RBFN), and adaptive neuro fuzzy inference system (ANFIS) schemes.