A NOVEL METHOD TO DETECT HUMAN DENTAL CAVITIES USING YOLOv11
P Prathiba1, R Meenatchi2
1Department of Computer Science and Engineering, 2Assistant Professor,Kathir College of Engineering
Coimbatore, India
Abstract: One of the most important facets of oral healthcare is the spotting of dental cavities, or caries, which are often done manually by qualified specialists utilizing radiography, visual inspection, or tactile methods. Despite their effectiveness, these traditional approaches are frequently arbitrary, labor-intensive, and prone to human mistake. In this study, we present a unique method for detecting dental cavities in humans by utilizing the You Only Look Once (YOLO) v11 architecture in conjunction with a deep learning-based object identification framework. A large dataset’s of dental images is used to train YOLOv11, a cutting-edge real-time object detection model, to detect cavities in their early stages, allowing for automatic and precise diagnosis. Our method makes use of YOLOv11’s high precision and low latency cavity detection and localization capabilities, even in complicated dental pictures. Training data for the suggested model comes from annotated images.
Keywords: Rice leaf disease detection, YOLOv10, deep learning, precision agriculture, machine learning, real-time disease diagnosis, plant disease classification, sustainable agriculture, convolutional neural networks (CNN), agricultural technology, food security, automated disease identification.