A Hybrid YOLOv8s+Swin-T Transformer Approach for Automated Caries Detection on Periapical Radiographs

Early dental caries detection is essential for timely diagnosis and treatment. However, current deep learning (DL) models exhibit inconsistent accuracy across different dental X-ray datasets, revealing limitations in their robustness and adaptability. To automate caries detection in intraoral periapical radiographs, this study presents a hybrid object detector that integrates a Swin-T transformer with a YOLOv8s backbone. The model was trained on 1887 radiographs collected from the Sibar Institute of Dental Sciences, Guntur. To detect dental caries in intricate intraoral structures, this work presents an improved feature extraction through its hierarchical attention mechanism that outperforms convolutional neural network (CNN)-based models in both spatial understanding and contextual awareness. We evaluated the method against single-stage YOLOv8 variants (n, s, m, l) and a representative two-stage detector (Faster R-CNN with ResNet-50-FPNv2) under a consistent protocol. The proposed YOLOv8s+Swin-T outperformed all baselines in precision, recall, F1-score, and mAP@0.5, achieving 0.97 for precision/recall/F1 and 0.99 for mAP@0.5. These results underscore the model's clinical applicability and robustness, providing a reliable tool for accurate caries detection and supporting routine AI-assisted diagnosis.