Enhanced Dental Caries Localization in Intraoral Photographs Through Hierarchical Feature Learning and Multi-Scale Fusion

Dental caries is a widespread oral disease whose
early detection remains difficult because conventional visual
assessment is subjective, time-consuming, and prone to examiner variability. This study proposes an automated detection
framework that combines deep residual feature extraction with
bidirectional multi-scale feature aggregation to improve localization of small and low-contrast carious lesions in intraoral
photographs. Transfer learning, image normalization, and geometric and photometric augmentation are employed to enhance
robustness. The framework is evaluated on the publicly available
Annotated Intraoral Image Dataset for Dental Caries Detection
and compared with several alternative backbone architectures.
Experimental results show a precision of 0.8173, recall of 0.7601,
F1-score of 0.7877, mAP@0.5 of 0.7998, and mAP@0.5:0.95
of 0.6361. Image-level evaluation further yields a sensitivity
of 0.8894, specificity of 0.9502, and accuracy of 0.9283. These
findings demonstrate effective lesion localization and clinically
relevant screening performance while providing a reproducible
benchmark for future research on reliable computer-aided dental
caries detection.