Dental Caries Detection Using Faster R-CNN and YOLO V3

Abstract

Deep learning techniques are gradually being utilized in many fields. Healthcare is a field in which deep learning can thrive. The study conducted focuses on using deep learning object detection models to detect dental cavities in an individual’s mouth. These images taken from a camera will be fed live to the object detection model to discover the precise coordinates of dental caries if it happens to exist. Previous studies depict that X-rays were often used for detecting dental caries. This study wants to put emphasis on avoiding the use of X-rays since they have a chance of harming human tissue, as well as, and they cannot detect hidden caries. Thus, it is necessary to detect dental caries in an accurate manner, with the proper tools. Studies have also conducted dental caries prediction using the frontal view of the images only. Some have made use of different angles for the images in the dataset, however, there still lies the problem of capturing the posterior teeth. Roughly 300 images get used, as the dataset, for the training and testing of the object detection model. 80% is used for training whereas 20% is used for testing. Two deep learning frameworks have been proposed to evaluate dental cavities, the You Only Once (YOLO) V3 object detection model and the Faster Region-Convolutional Neural Network object detection model. Our results show that the YOLO V3 model consists of an accuracy of 75%, while Faster R-CNN had an accuracy of 80%. The sensitivity values of YOLO V3 and Faster R-CNN were 76% and 73% respectively. The model with better performance would be used for future development of the product, along with the hardware components. Our hardware components aim to take images from outside the mouth, for the frontal teeth, and take images from inside the mouth, for the posterior teeth.