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

## TL;DR

This paper evaluates deep learning models for classifying dental conditions from panoramic X-ray images, demonstrating that hybrid CNN-ensemble models outperform individual CNNs and pre-trained architectures in accuracy.

## Contribution

It introduces a hybrid CNN-Random Forest model for dental condition classification, showing improved performance over traditional CNNs and pre-trained models.

## Key findings

- Hybrid CNN-Random Forest achieved 85.4% accuracy.
- Pre-trained VGG16 reached 82.3% accuracy.
- Hybrid models enhance discrimination of similar dental conditions.

## 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.