Dental Cavity Detection Using U-NetArchitecture

Dental cavities, which are sometimes referred to as dental caries or tooth decay, are one of the most common chronic illnesses globally and provide serious obstacles to dental treatment. The aim is to provide a proactive approach to oral health by identifying and preventing dental problems early on through the use of cavity detecting technologies. By utilizing deep learning techniques more especially, the U-Net architecture the dental cavity detection seeks to create a reliable system for the early and accurate identification of dental cavities. The system seeks to identify different dental disorders from photos utilizing the U-Net architecture with a focus on cavity identification. Model training and evaluation are made easier with the use of an annotated dataset of grayscale images. The U-Net model is trained and tuned to efficiently separate and identify dental cavities from X-ray and normal dental images by utilizing avaried dataset of dental images that have been labelled with disease information. Furthermore, accessibility is improved via a user-friendly interface, which makes it easier for people to access and use the dental detection system.