Stage-wise Dental Caries Detection Using Deep Learning

Abstract

Dental caries, also known as tooth decay, is one of the most common long-term oral health problems that affects people of all ages, especially children. Catching dental caries early and accurately is very important to stop serious tooth damage, pain, and expensive treatments. Usually, dentists use visual checks and X-rays to look for caries, but these methods can be subjective, take a long time, and are not always available in many areas. Recently, there have been large improvements in deep learning and computer vision, which have made it possible to use automated tools for analyzing dental images and also making accurate diagnoses. This paper introduces a step-by-step system for detecting dental caries with the help of deep learning, built as a web-based tool. The system uses a convolutional neural network with a ResNet-50 structure and adds Feature Pyramid Network (FPN) to better capture detailed features using dental images. The network is designed to not only find if there is caries but also to classify it into three different levels, such as early, moderate, and severe. To make the model more reliable as well as effective, image preprocessing and data enhancement techniques have been used. The web interface allows users and dentists to upload images to get quick diagnostic results, along with visual heatmaps that show the areas of concern. The results of the experiments show that this method has a high accuracy of about 95%, which is far better than traditional approaches. The system offers an efficient, scalable, and easy-to-use solution for early detection of dental caries, helping to promote preventive care and reduce the need for manual diagnosis by dental professionals.