An intelligent deep network for dental medical image processing system

Abstract

Nowadays, many people are affected by oral health issues because of continuous changes in lifestyle such as personal speech that is affected by crooked teeth and malocclusion teeth. Moreover, a dental problem can cause bacterial infections, cavities, and many other diseases due to an improper lifestyle. In this research, a novel Intelligent Ant Lion-based Convolution Neural Model (IALCNM) is designed for segmenting affected parts in teeth and to classify the wear and periodontitis diseases from the collected dataset. Moreover, the developed technique is implemented in the python 3.8 environment and the attained results of the developed procedure are related to other existing techniques implemented for different diseases in standings of accuracy, precision, error rate, execution time, and so on. Hence the outcome indicates that the current research technique applied to self-created datasets has enhanced the accuracy of segmenting affected parts and disease prediction more than other techniques.