A Computer-Aided Model for Dental Image Diagnosis Utilizing Convolutional Neural Networks

Abstract

A Convolutional Neural Network (CNN) is an artificial neural network that is primarily utilized for the purposes of image recognition and processing, owing to its remarkable ability to recognize patterns within images. CNNs have found widespread application in diverse areas of computer vision, including but not limited to object tracking and recognition, security, and military and biomedical image analysis. CNN in orthodontic medical imaging technologies to reduce orthodontic treatment planning time, including automatic landmark search on cephalometric radiographs, cone beam computed tomography (CBCT) tooth segmentation, and CBCT tooth segmentation. This paper describes the strategy and the architecture of deep convolutional neural networks applied to DICOM datasets to distinguish between X-ray pictures with and without teeth. This work focuses on the application of the CNNs to a DICOM dataset in orthodontics as pre-processing for CNNs using fuzzy C-means clustering to construct a reasonable prediction that results in improved accuracy of this system. The aforementioned proposal has shown encouraging outcomes and visual representations, indicating that utilizing methods based on convolutional neural networks can greatly enhance the computational planning of orthodontic treatments by decreasing the time required for analysis. In many cases, this approach's analysis surpasses the accuracy of a manual orthodontist. This model achieved an accuracy exceeding 98% in applying the CNNs to differentiate between X-ray images with teeth and others with no teeth to focus any further work only on useful images which helps in diagnosis planning.