Work

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

Year: 2026

Type: article

Abstract: 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.

Source: Scientific Reports

DOI: 10.1038/s41598-026-62329-1

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

Institutions: Iqra University, Wor-Wic Community College, National University of Sciences and Technology, King Abdulaziz University

Language: English

Topic: Dental Radiography and Imaging

Subfield: Oral Surgery

Field: Dentistry

Domain: Health Sciences

SDG: Good health and well-being

Open Access status: gold

Estimated APC paid: $2,690