Mask RCNN with RESNET50 for Dental Filling Detection

Abstract

Teeth are very important for humans to eat food. However, teeth do get damaged for several reasons, like poor maintenance. Damaged teeth can cause severe pain and make it difficult to eat food. To safeguard the tooth from minor damages, an inert material is used to close the gap between the live part of the teeth or sometimes even the nerve and enamel. Although, long-time ignorance can increase the damage and inevitably result in root canal or tooth replacement. In the case of root canal, the gap between nerve and enamel is filled with an inert material. To check if the filling has been done properly, an X-ray is taken and verify. As technology is developing, robots are being introduced into many fields. In the medical field, there are instances where robots do surgery. For dental treatment, as an X-ray is taken to determine the filing, this work introduces a model to analyze the X-ray taken and estimate the level of filing done. The model is constructed using Mask RCNN with ResNet50 architecture. A dataset of different kinds of filings is taken and trained using the model. This model can be used to enable machines to perform dental operations as it works on pixel-level classification.