DENTAL IMAGE PROCESSING FOR CAVITY DETECTION AND RESTORATION PLANNING

Abstract

Dental image processing is a rapidly evolving field that plays a crucial role in the automation of cavity detection and assists in comprehensive restoration planning. The accurate identification of dental caries, commonly referred to as cavities, is essential for early diagnosis and effective treatment, thereby preventing further complications such as tooth decay, infection, or loss. Traditional methods of cavity detection rely heavily on visual inspections by dentists and manual interpretation of radiographic images, which can be subjective, time-consuming, and prone to human error. Therefore, the development of automated and intelligent diagnostic systems using advanced machine learning techniques has gained significant attention in the dental and medical imaging communities.
This paper presents a novel method for detecting cavities in dental radiographs and intraoral images using Convolutional Neural Networks (CNN), a deep learning architecture known for its exceptional ability to recognize patterns and extract meaningful features from visual data. The proposed approach capitalizes on CNN’s capability to analyse complex image structures, identify potential regions of interest, and classify them with high precision. By training on a diverse dataset of dental X-rays and intraoral scans, the model effectively learns to differentiate between healthy and decayed regions, providing a reliable and automated solution for dental diagnostics.
The developed system operates in multiple stages. Initially, the input dental images undergo preprocessing to enhance image quality and remove noise, ensuring that the model receives clear and standardized data for analysis. Subsequently, the CNN model extracts relevant features from the images, such as variations in texture, intensity, and structural patterns associated with dental caries.