DEEP CONVOLUTIONAL NEURAL NETWORKS FOR CAVITY AND PIT DETECTION IN DENTAL IMAGES

Abstract

If it is not addressed, several oral issues can arise from dental caries, one of the most prevalent oral disorders. However, access to professional dental care is often limited, particularly in underserved communities. Our proposed AI-based solution empowers individuals to monitor their oral health and detect early signs of cavities. Children are particularly susceptible to pits and caries in permanent molars, which mostly arise in the cavities on the occlusal, buccal, and palatal surfaces of molars. To address this challenge, we propose developing an AI-powered solution that utilizes smartphone cameras to capture and analyze dental images for cavity detection. This research enables image processing techniques and computer vision algorithms to identify and classify various cavities, including early-stage lesions and enamel defects. Using the Teeth Cave Convolutional Neural Network (CAVTee-CNN), which was particularly designed for dental cavity detection, we were able to minimize information loss during the preprocessing of images. This custom network was developed through extensive experimentation with convolutional layers, activation functions, and pooling mechanisms adapted to highlight dental features. The CAVTee-CNN Model enhances both efficiency and accuracy; it yields the best result of 85.2. By providing timely and accurate cavity detection, the application can empower users to make informed decisions about their oral health and seek appropriate dental care when necessary.