New bag of features using reinforcement Aquila optimization and weighted Bayesian Gaussian mixture modelling for dental images

摘要

Dentistry diseases are worldwide concern with the fact that 5 of medical budget isspent on it. Dental diagnosis requires frequent visits to clinic and multiple personalcheck-ups by an expert. This delays the process of diagnosis as well as introduces thedanger of oral infection spread. Automation is boon to speed up the process of diagnosis,reduce expert's involvement and help in handling volume of patients. In situationslike pandemic as seen in this decade, computer based automatic systems inhealth have proven their importance and necessity. For dental diagnosis, images areuseful tool for better anatomical views and accurate decision of treatment. However,manual dental analysis increases load on dentists for initial check-up that can be easilyperformed with automated systems or self-kits. Oral cavity is a dental illness, ifnot identified and treated on time may lead to other serious ailments. This work presentsa framework that performs tooth image classification for cavity and non-cavitywith new bag of features (NBoF) method. NBoF method is an attempt to improvethe performance of bag of features (BoF) using the proposed reinforcement Aquilaoptimization (RAO) and weighted Bayesian Gaussian mixture modelling (WBGMM).An analysis of the performance of the NBoF using the proposed RAO and WBGMMis conducted using standard metrics. The comparative study of results proves thatthe proposed NBoF method outperforms the existing state-of-the-art algorithms.