Back to all papers

Multi-label diagnosis of dental conditions from panoramic x-rays using attention-enhanced deep learning.

Authors

Raeisi Z,Rokhva S,Rahmani F,Goodarzi A,Najafzadeh H

Affiliations (4)

  • Department of Computer Science, University of Fairleigh Dickinson, Vancouver Campus, Vancouver, Canada.
  • Department of Information Technology, Faculty of Industrial and System Engineering, Tarbiat Modares Univeristy, Tehran, Iran.
  • Department of Oral and Maxillofacial Surgery, School of Dentistry, Shiraz University of Medical Sciences, Shiraz, Iran.
  • Department of Medical Bioengineering, Faculty of Advanced Medical Sciences, Tabriz University of Medical Sciences, Tabriz, Iran. [email protected].

Abstract

This study aimed to develop and evaluate automated deep learning models for multi-class classification of dental conditions in panoramic X-ray images, comparing the effectiveness of custom CNN architectures with attention mechanisms, pre-trained models, and hybrid approaches. A dataset of 1,512 panoramic dental X-rays was preprocessed through segmentation, creating 4,764 class-balanced images across four categories: Fillings, Cavity, Implant, and Impacted Tooth. Data augmentation and preprocessing techniques including brightness adjustment, CLAHE enhancement, and normalization were applied. Multiple architectures were evaluated: custom CNN with attention mechanism, pre-trained models (VGG16, ResNet50, Xception) with attention integration, and hybrid CNN-machine learning approaches (CNN + SVM, CNN + Random Forest, CNN + Decision Tree). Performance was assessed using 5-fold cross-validation with accuracy, precision, recall, F1-score, and ROC-AUC metrics. The hybrid CNN + Random Forest model with preprocessing achieved the highest performance: 90.6% accuracy, 0.987 ROC-AUC, and 0.906 F1-score. Preprocessing consistently improved performance across all architectures, with accuracy gains ranging from 6.3% (VGG16) to 19.4% (ResNet50). The custom CNN with attention mechanism reached 86.0% accuracy, outperforming conventional CNN approaches (76.0%). Among pre-trained models, Xception with preprocessing achieved 79.8% accuracy. Hybrid CNN-machine learning approaches demonstrated superior performance for dental condition classification compared to end-to-end deep learning models. However, clinical implementation requires addressing the dataset limitation of lacking normal/healthy cases and conducting prospective validation studies across diverse clinical populations to establish real-world effectiveness and safety.

Topics

Deep LearningRadiography, PanoramicTooth DiseasesJournal Article

Ready to Sharpen Your Edge?

Join hundreds of your peers who rely on RadAI Slice. Get the essential weekly briefing that empowers you to navigate the future of radiology.

We respect your privacy. Unsubscribe at any time.