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

--This study aimed to develop and evaluate multiple deep learning approaches for automated classification of dental conditions in panoramic radiographs, comparing the performance of custom convolutional neural networks (CNNs), hybrid CNN-machine learning models, and fine-tuned pre-trained architectures for detecting fillings, cavities, implants, and impacted teeth. A dataset of 1,512 panoramic dental X-ray images containing 11,137 annotations across four dental conditions was employed, with class imbalance addressed through random down-sampling to create a balanced dataset of 894 samples per condition. Multiple computational approaches were implemented and evaluated using 5-fold cross-validation, including a custom CNN architecture, hybrid models combining CNN feature extraction with traditional machine learning classifiers (Support V ector Machine, Decision Tree, and Random Forest), and three fine-tuned pre-trained architectures (VGG16, Xception, and ResNet50). Performance evaluation was conducted using standard classification metrics including accuracy, precision, recall, and F1-score. The hybrid CNN-Random Forest model achieved the highest performance with 85.4 2.3% accuracy, representing an 11 percentage point improvement over the custom CNN baseline (74.29%). Among pre-trained architectures, VGG16 demonstrated superior performance with 82.3 2.0% accuracy, followed by Xception (80.9 2.3%) and ResNet50 (79.5 2.7%). The CNN+Random Forest model exhibited exceptional performance for fillings detection (F1-score: 0.860 0.033) and maintained balanced classification across all dental conditions. Systematic misclassifica-tion patterns were observed between morphologically similar conditions, particularly cavity-implant and cavity-impacted tooth categories, highlighting the inherent challenges in distinguishing overlapping dental pathologies. Hybrid CNN-based approaches, particularly the combination of CNN feature extraction with Random Forest classification, provide enhanced discriminative capability for automated dental condition detection compared to standalone architectures.