Revolutionizing Dental Caries Detection: An In-Depth Study of Neural Network Approaches

Dental caries, a progressive bacterial infection, stands as a leading cause of tooth loss, primarily stemming from inadequate dental hygiene practices. This pervasive issue gives rise to various other dental afflictions. The primary objective of this project is to advance early-stage detection of dental caries through the utilization of digital color imagery, facilitating more straightforward and effective treatment protocols. The proposed classification methodology holds promise for tele-dentistry, aligning with the burgeoning field of tele-informatic oral healthcare. To achieve this, we have implemented a Convolutional Neural Network (CNN) as the deep learning model of choice. Extensive training and testing were conducted on a binary dataset encompassing images both with and without caries. Remarkably, our results show an impressive accuracy rating of 99.13%. This remarkable outcome underscores the effectiveness of the MI-DCNNE model, illustrating its potential to significantly enhance the classification of dental caries. The findings of this research represent a crucial step towards revolutionizing early-stage detection of dental caries, offering a profound impact on oral healthcare practices and outcomes.