Three‐dimensional dental image segmentation and classification using deep learning with tunicate swarm algorithm

Abstract

AbstractDentistry frequently makes use of intraoral scanning technologies to digitally acquire the three‐dimensional (3D) geometry of teeth. In recent times, dental clinics over the globe utilize used computer aided diagnosis (CAD) models to make treatment plans, for example, orthodontics. Orthodontic CAD system acts as a vital part of the advanced dentistry field. A 3D dental model, computed by patient impression, as input and aids dentist in the extraction, moving, deletion, and rearranging of teeth to simulate treatment output. Tooth segmentation and labelling is the basic and foremost element of the CAD model which needs to be addressed. Automated segmentation and classification of 3D dental images using advanced machine learning and deep learning (DL) models become essential. This article introduces a new 3D dental image segmentation and classification using DL with tunicate swarm algorithm (3DDISC‐DLTSA) model. The major intention of the 3DDISC‐DLTSA system is to segment the tooth model and identify seven distinct tooth types. To accomplish this, the presented 3DDISC‐DLTSA model performs image pre‐processing in two stages namely image filtering and U‐Net segmentation. In addition, the 3DDISC‐DLTSA model derives DenseNet‐169 model for feature extraction purposes. For the recognition and classification of tooth type, the TSA based hyperparameter tuning process is carried out which helps to accomplish maximum classification performance. A wide range of experimental analyses is performed and the outcomes are inspected under many aspects. On dataset‐1, 3DDISC‐DLTSA model accuracy rose by 96.67%. On dataset‐3, 3DDISC‐DLTSA model accuracy rose by 97.48% and algorithm accuracy by 97.35%. The 3DDISC‐DLTSA model outperformed more modern models, according to the comparative investigation.