BMC oral health · 2026
Enhancing YOLOv8s with MHSA and GA for robust caries detection on periapical radiographs.
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Meghana D, Manimaran A
Аннотация
BACKGROUND: Early identification of dental caries on Intraoral Periapical Radiographs is clinically important, yet automated detectors often show unstable performance across datasets due to subtle lesion appearance and limited generalization.
METHODS: We propose an attention-augmented YOLOv8s detector that inserts two Multi-Head Self-Attention (MHSA) blocks in the deeper backbone and couples this architecture with a compact Genetic Algorithm (GA)-based schedule search to stabilize optimization. Experiments were conducted on a curated dataset collected at Sibar Institute of Dental Sciences, Guntur (1,887 radiographs), standardized and quality screened, and split into 80% for training, 10% for validation, and 10% for testing.
RESULTS: Using identical augmentations and a fixed inference operating point, the proposed YOLOv8s+MHSA+GA achieves Precision 0.98, Recall 0.94, F1 Score 0.96, mAP@0.5 0.97, and mAP@0.5:0.95 0.94. Relative to the next-best baseline (per metric), this corresponds to +0.21 mAP@0.5 (0.97 vs 0.76), +0.61 mAP@0.5:0.95 (0.94 vs 0.33), and +0.26 Recall (0.94 vs 0.68). We further report Wilson 95% confidence intervals for precision and recall to quantify statistical uncertainty.
CONCLUSIONS: These results indicate that deeper-stage self-attention, paired with principled schedule selection, delivers clinically meaningful gains in sensitivity and localization tightness while preserving the efficiency advantages of a one-stage detector, supporting routine AI-assisted diagnosis.
METHODS: We propose an attention-augmented YOLOv8s detector that inserts two Multi-Head Self-Attention (MHSA) blocks in the deeper backbone and couples this architecture with a compact Genetic Algorithm (GA)-based schedule search to stabilize optimization. Experiments were conducted on a curated dataset collected at Sibar Institute of Dental Sciences, Guntur (1,887 radiographs), standardized and quality screened, and split into 80% for training, 10% for validation, and 10% for testing.
RESULTS: Using identical augmentations and a fixed inference operating point, the proposed YOLOv8s+MHSA+GA achieves Precision 0.98, Recall 0.94, F1 Score 0.96, mAP@0.5 0.97, and mAP@0.5:0.95 0.94. Relative to the next-best baseline (per metric), this corresponds to +0.21 mAP@0.5 (0.97 vs 0.76), +0.61 mAP@0.5:0.95 (0.94 vs 0.33), and +0.26 Recall (0.94 vs 0.68). We further report Wilson 95% confidence intervals for precision and recall to quantify statistical uncertainty.
CONCLUSIONS: These results indicate that deeper-stage self-attention, paired with principled schedule selection, delivers clinically meaningful gains in sensitivity and localization tightness while preserving the efficiency advantages of a one-stage detector, supporting routine AI-assisted diagnosis.