A Deep Learning Framework with Pruning RoI Proposal for Dental Caries Detection in Panoramic X-Ray Images

EasyChair Preprint 11335

Abstract

Dental caries is a prevalent noncommunicable disease that affects over half of the global population. It can significantly diminish individuals' quality of life by impairing their eating and socializing abilities. Consistent dental check-ups and professional oral healthcare are crucial in preventing dental caries and other oral diseases. Deep learning based object detection provides an efficient approach to assist dentists in identifying and treating dental caries. In this paper, we present a deep learning framework with a lightweight pruning region of interest (P-RoI) proposal specifically designed for detecting dental caries in panoramic dental radiographic images. Moreover, this framework can be enhanced with an auxiliary head for label assignment during the training process. By utilizing the Cascade Mask R-CNN model with a ResNet-101 backbone as the baseline, our modified framework with the P-RoI proposal and auxiliary head achieves a notable 3.85 increase in Average Precision (AP) for the dental caries class within our dental dataset.

Keyphrases: Dental caries, Label Assignment, Pruning RoI proposal, object detection

@booklet{EasyChair:11335,
  author    = {Xizhe Wang and Jing Guo and Peng Zhang and Qilei Chen and Zhang Zhang and Yu Cao and Xinwen Fu and Benyuan Liu},
  title     = {A Deep Learning Framework with Pruning RoI Proposal for Dental Caries Detection in Panoramic X-Ray Images},
  howpublished = {EasyChair Preprint 11335},
  year      = {EasyChair, 2023}}