RCTE: A multi-class object detection framework for dental panoramic radiographs

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. Results: Compared with the original YOLOv8n baseline, RCTE improved mAP50, mAP75, mAP50–95, and Recall by 2.55, 3.88, 2.40, and 5.10 percentage points, respectively. The proposed framework achieved better detection completeness and localization accuracy compared with other YOLO-based detectors. Furthermore, RCTE maintained real-time inference capability, demonstrating a favorable balance between detection accuracy and computational efficiency. Conclusion: The proposed RCTE framework effectively improves multi-class object detection performance in dental panoramic radiographs by enhancing cross-scale feature interaction, multi-scale feature fusion, and detection feature recalibration. This method provides a potential solution for computer-aided dental image analysis.