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<p>Automated analysis of dental radiographs can improve the efficiency and consistency of diagnostic workflows. This study proposes a deep learning and federated learning framework for dental arch classification using panoramic radiographs from the DenPAR dataset. A pre-trained ResNet50 model was employed for feature extraction, while CatBoost, Support Vector Machine (SVM), and Recurrent Neural Network (RNN) classifiers were evaluated for classification. A privacy-preserving Federated Learning approach based on FedBM was also implemented to enable collaborative model training without sharing raw data. Experimental results demonstrated strong classification performance, with CatBoost and SVM achieving 98% accuracy, while the federated model achieved 99% accuracy on the test set. Evaluation using precision, recall, specificity, F1-score, ROC-AUC, and confusion matrices further confirmed the robustness of the proposed framework. The results indicate that deep feature extraction combined with federated learning provides an effective and privacy-preserving solution for automated dental arch classification in panoramic radiographs.</p>

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Dental arch classification using deep learning and federated learning on DenPAR radiographs

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年7月22日

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  • Abdullah Qureshi,
  • Qurat Ul Ain Aini,
  • Evgeny Solomin,
  • Nadeem Ahmad,
  • Zaki Uddin,
  • Ahmad H. Milyani

摘要

Automated analysis of dental radiographs can improve the efficiency and consistency of diagnostic workflows. This study proposes a deep learning and federated learning framework for dental arch classification using panoramic radiographs from the DenPAR dataset. A pre-trained ResNet50 model was employed for feature extraction, while CatBoost, Support Vector Machine (SVM), and Recurrent Neural Network (RNN) classifiers were evaluated for classification. A privacy-preserving Federated Learning approach based on FedBM was also implemented to enable collaborative model training without sharing raw data. Experimental results demonstrated strong classification performance, with CatBoost and SVM achieving 98% accuracy, while the federated model achieved 99% accuracy on the test set. Evaluation using precision, recall, specificity, F1-score, ROC-AUC, and confusion matrices further confirmed the robustness of the proposed framework. The results indicate that deep feature extraction combined with federated learning provides an effective and privacy-preserving solution for automated dental arch classification in panoramic radiographs.