Abstract: Assessing the quality of panoramic dental radiographs is essential to ensure diagnostic accuracy and patient safety. However, existing CNN-based approaches for radiograph quality assessment often emphasize architectural comparisons, while providing limited discussion on training stability and generalization, particularly when applied to relatively small and heterogeneous datasets. To address this gap, this study proposes a transfer learning-based framework that integrates Global Average Pooling (GAP) and Batch Normalization (BN) to enhance feature robustness and reduce overfitting in panoramic dental radiograph quality classification. Three pretrained CNN architectures: ResNet50, VGG16, and VGG19 were evaluated using panoramic radiographs collected from two tertiary hospitals in Indonesia. Experimental results using k-fold cross-validation indicate that the proposed GAP+BN refinement improves classification consistency across models, with VGG16 demonstrating the most stable and reliable performance. These findings suggest that domain-adapted transfer learning with appropriate feature aggregation and normalization can support the development of automated and clinically reliable quality assurance systems for panoramic dental imaging.

Nur Nafiiyah, Rifky Aisyatul Faroh, Eha Renwi Astuti, Rini Widyaningrum, Agus Harjoko, Kang-Hyun Jo, Alhidayati Asymal and Youan Nhareswary Dwike Prasetya. “Automated Quality Evaluation of Panoramic Dental Radiographs Using a Domain-Adapted Transfer Learning”. International Journal of Advanced Computer Science and Applications (IJACSA) 16.12 (2025). http://dx.doi.org/10.14569/IJACSA.2025.0161227

@article{Nafiiyah2025,
title = {Automated Quality Evaluation of Panoramic Dental Radiographs Using a Domain-Adapted Transfer Learning},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2025.0161227},
url = {http://dx.doi.org/10.14569/IJACSA.2025.0161227},
year = {2025},
publisher = {The Science and Information Organization},
volume = {16},
number = {12},
author = {Nur Nafiiyah and Rifky Aisyatul Faroh and Eha Renwi Astuti and Rini Widyaningrum and Agus Harjoko and Kang-Hyun Jo and Alhidayati Asymal and Youan Nhareswary Dwike Prasetya}
}