Hybrid Transfer Learning for Diagnosing Teeth Using Panoramic X-rays
| dc.contributor.author | Dr. Mostafa Elgayar | |
| dc.date.accessioned | 2025-12-18T09:44:23Z | |
| dc.date.issued | 2024-12 | |
| dc.description.abstract | The increasing focus on oral diseases has highlighted the need for automated diagnostic processes. Dental panoramic X-rays, commonly used in diagnosis, benefit from advancements in deep learning for efficient disease detection. The DENTEX Challenge 2023 aimed to enhance the automatic detection of abnormal teeth and their enumeration from these Xrays. We propose a unified technique that combines direct classification with a hybrid approach, integrating deep learning and traditional classifiers. Our method integrates segmentation and detection models to identify abnormal teeth accurately. Among various models, the Vision Transformer (ViT) achieved the highest accuracy of 97% using both approaches. The hybrid framework, combining modified U-Net with a Support Vector Machine, reached 99% accuracy with fewer parameters, demonstrating its suitability for clinical applications where efficiency is crucial. These results underscore the potential of AI in improving dental diagnostics. | |
| dc.identifier.uri | https://research.arabeast.edu.sa/handle/123456789/492 | |
| dc.language.iso | en | |
| dc.publisher | International Journal of Advanced Computer Science and Applications | |
| dc.title | Hybrid Transfer Learning for Diagnosing Teeth Using Panoramic X-rays | |
| dc.type | Article |