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

2025-08-27

AI Summary

This study addresses the automatic classification of dental pathologies—including restorations, caries, dental implants, and impacted teeth—in panoramic radiographs. We propose a hybrid CNN–random forest model: deep features are first extracted using pretrained CNNs (e.g., VGG16), then fed into a random forest classifier for final decision-making. Under five-fold cross-validation, the hybrid model achieves 85.4% accuracy, outperforming both a custom-designed CNN (74.3%) and fine-tuned VGG16 alone (82.3%), particularly in distinguishing morphologically similar lesions. Our key contribution lies in empirically validating the synergistic efficacy of deep feature representations with traditional ensemble classifiers—bridging high discriminative power and inherent interpretability. This approach establishes a novel paradigm for automated dental image analysis that balances diagnostic accuracy with clinical transparency, offering practical potential for computer-aided diagnosis in dentistry.

Technology Category

• Computer Vision: Segmentation
• Machine Learning: Ensemble Methods
• Knowledge Representation and Reasoning: Diagnosis and Abductive Reasoning

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Web Mining and Content Analysis: Large pretrained models with web data
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

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.

Problem

• Automated classification of dental conditions in X-rays
• Evaluating deep learning methods for dental diagnosis
• Improving discrimination of morphologically similar dental conditions

Innovation

• Hybrid CNN-Random Forest model
• Fine-tuned pre-trained VGG16 architecture
• CNN feature extraction with ensemble classifiers