ABSTRACT
Age is considered as an essential factor for all individuals in their lives. Identification of individuals is also considered as a vital feature of legal dentistry than medicine. Gender, age and race are determined by the documentation of individuals. Chronological age is measured as the birth date for registration. Age is an essential factor in court law, clinics and research. Foremost dental clues are essentially considered to resolve crime. Age is evaluated based on dental age (DA), bone age and mental age. DA is more vital for tooth development illustration than other development characteristics and has low variability related to chronological age. Therefore, DA is considered a significant factor for establishing individuals’ age. So, an efficient Elman Neural Network (ENN) with Dragonfly Optimization (DO) algorithm is proposed for DA classification. Initially, an Orthopantomogram (OPG) input image is pre-processed for image smoothing and noise reduction with Improved Kaun Filter (IKF). Teeth from image are portioned with Improved Artificial Bee Colony (IABC) clustering and morphological post-processing outcome to enhance precision. Specific characteristics are extracted to upgrade precision. Age is classified with ENN–DO. With ENN, DO is utilized to resolve the training issues. The DA can be predicted the chronological age from 4 to 18 years of South Indian subject used it for various applications. Experimental outcomes demonstrate that ENN–DO acquires better accuracy than the existing classifiers such as ELM-SRC, MELM-SRC and RBFN. The DA of an individual can be predicted up to 18 years of age for various applications. In future, other classification schemes will be evaluated by using large dataset of teeth images and will be also predicted for all ages.
