Automated Detection of Dental Caries Using Deep Learning Models
Ali, A., Amer, H., Abdelsalam, M., Saraya, M. (2025). Automated Detection of Dental Caries Using Deep Learning Models. EKB Journal Management System, 10(1), -. doi: 10.21608/njccs.2025.415897.1053
Ali Qasim Ali; Hanan M. Amer; Mohamed M. Abdelsalam; Mohamed S. Saraya. "Automated Detection of Dental Caries Using Deep Learning Models". EKB Journal Management System, 10, 1, 2025, -. doi: 10.21608/njccs.2025.415897.1053
Ali, A., Amer, H., Abdelsalam, M., Saraya, M. (2025). 'Automated Detection of Dental Caries Using Deep Learning Models', EKB Journal Management System, 10(1), pp. -. doi: 10.21608/njccs.2025.415897.1053
Ali, A., Amer, H., Abdelsalam, M., Saraya, M. Automated Detection of Dental Caries Using Deep Learning Models. EKB Journal Management System, 2025; 10(1): -. doi: 10.21608/njccs.2025.415897.1053

Automated Detection of Dental Caries Using Deep Learning Models

Nile Journal of Communication and Computer Science
Volume 10, Issue 1, December 2025    PDF (900.52 K)
Document Type: Original Article
DOI: 10.21608/njccs.2025.415897.1053
Authors
Ali Qasim Ali* 1; Hanan M. Amer2; Mohamed M. Abdelsalam1; Mohamed S. Saraya1
1Computers and control systems department Mansoura University faculty of engineering, Mansoura University, Mansoura, Egypt
2Electronics and Communications Engineering (ECE) Department, Faculty of Engineering, Mansoura University, Mansoura, Egypt
Abstract
Dental caries is a disease that is common worldwide. Dental caries remains one of the most prevalent oral diseases globally, necessitating accurate and early detection to enable timely interventions and preserve tooth structure. This study presents a lightweight and task-specific Convolutional Neural Network (CNN) architecture, called DentalNet-Lite, designed for the multiclass classification of 1200 RGB intraoral images into three diagnostic categories: normal tooth, early dental caries, and advanced dental caries. The model DentalNet-Lite is a custom CNN designed for the automated detection of dental caries. The model was trained and evaluated, benchmarked against five pretrained CNNs: MobileNetV2, DenseNet121, ResNet50V2, Xception, and InceptionResNetV2. employing accuracy, precision, recall, and F1-score as evaluation criteria. DentalNet-Lite achieved a test accuracy of 99.07%, exceeding all competing approaches while maintaining low computing complexity, hence demonstrating its suitability for real-time, resource-limited clinical applications. These results highlight the potential of the custom-designed model to assist dental professionals in clinical diagnosis and improve patient outcomes in diverse healthcare settings.
Keywords
Keywords: Dental Caries; Deep Learning; Convolutional Neural Networks (CNN); AI in Dentistry; Early Detection
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