Advanced Deep Learning Techniques for Classifying Dental Conditions Using Panoramic X-Ray Images

• URL: http://arxiv.org/abs/2508.21088v1
• Date: Wed, 27 Aug 2025 04:52:50 GMT
• Title: Advanced Deep Learning Techniques for Classifying Dental Conditions Using Panoramic X-Ray Images
• Authors: Alireza Golkarieh, Kiana Kiashemshaki, Sajjad Rezvani Boroujeni,
• Abstract summary: This study investigates deep learning methods for automated classification of dental conditions in panoramic X-ray images.<n>Three approaches were evaluated: a custom convolutional neural network (CNN), hybrid models combining CNN feature extraction with traditional classifiers, and fine-tuned pre-trained architectures.<n>Results show that hybrid models improve discrimination of morphologically similar conditions and provide efficient, reliable performance.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This study investigates deep learning methods for automated classification of dental conditions in panoramic X-ray images. A dataset of 1,512 radiographs with 11,137 expert-verified annotations across four conditions fillings, cavities, implants, and impacted teeth was used. After preprocessing and class balancing, three approaches were evaluated: a custom convolutional neural network (CNN), hybrid models combining CNN feature extraction with traditional classifiers, and fine-tuned pre-trained architectures. Experiments employed 5 fold cross validation with accuracy, precision, recall, and F1 score as evaluation metrics. The hybrid CNN Random Forest model achieved the highest performance with 85.4% accuracy, surpassing the custom CNN baseline of 74.3%. Among pre-trained models, VGG16 performed best at 82.3% accuracy, followed by Xception and ResNet50. Results show that hybrid models improve discrimination of morphologically similar conditions and provide efficient, reliable performance. These findings suggest that combining CNN-based feature extraction with ensemble classifiers offers a practical path toward automated dental diagnostic support, while also highlighting the need for larger datasets and further clinical validation.

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