PloS one
RCTE: A multi-class object detection framework for dental panoramic radiographs
By Peng J, Liu J, Shen Y and 6 others.
Abstract
Background
Dental panoramic radiographs play an important role in oral disease screening, computer-aided diagnosis, and clinical treatment planning. However, accurate multi-class detection remains challenging due to small lesion sizes, ambiguous anatomical boundaries, insufficient multi-scale feature representation, and missed detections of complex dental structures.
Objective
This study aims to develop an accurate and efficient multi-class object detection framework for dental panoramic radiographs to improve the detection performance of small lesions, weak-boundary structures, and complex anatomical targets.
Methods
A novel re-parameterized cross-scale attention-enhanced framework, named RCTE, was proposed based on the YOLOv8n detector. The proposed framework integrates three complementary components: a Cross-Scale Channel Transformer (CSCT) module for cross-scale contextual interaction among P3, P4, and P5 features, a RepNCSPELAN4-based Re-parameterized Feature Pyramid Fusion (RPF) structure for enhancing multi-scale feature aggregation, and a Multi-Scale EMA (MS-EMA) mechanism for feature recalibration before the detection head. Experiments were conducted on a publicly available dental panoramic radiograph dataset containing 11 categories of dental structures and lesions. Model performance was evaluated using Precision, Recall, F1-score, mAP50, mAP75, and mAP50-95.