{
  "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.",
  "abstract_shown": true,
  "affiliations": [
    "Department of Computing & Technology, Iqra University",
    "Faculty of Computing and Technology, Iqra University",
    "Department of Electric Stations, Grids and Systems of Power Supply",
    "Department Head and Professor of Computer Science and Technology Department, Wor-wic Community College",
    "College of Electrical and Mechanical Engineering, National University of Science and Technology",
    "Department of Electrical and Computer Engineering, King Abdulaziz University"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 6,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Abdullah Qureshi"
    },
    {
      "affs": [
        1
      ],
      "name": "Qurat Ul Ain Aini"
    },
    {
      "affs": [
        2
      ],
      "name": "Evgeny Solomin"
    },
    {
      "affs": [
        3
      ],
      "name": "Nadeem Ahmad"
    },
    {
      "affs": [
        4
      ],
      "name": "Zaki Uddin"
    },
    {
      "affs": [
        5
      ],
      "name": "Ahmad H. Milyani"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/2ctwnthgpqp58.bib",
  "cited_by": 0,
  "doi": "10.1038/s41598-026-62329-1",
  "fulltext_url": "https://doi.org/10.1038/s41598-026-62329-1",
  "id": "2ctwnthgpqp58",
  "issns": [
    "2045-2322"
  ],
  "landing_url": "https://doi.org/10.1038/s41598-026-62329-1",
  "language": "en",
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "journal",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "lcc",
      "Q"
    ],
    [
      "lcc",
      "R"
    ],
    [
      "openalex",
      "3504|Dental Radiography and Imaging"
    ]
  ],
  "openalex": "W7170080965",
  "pdf_url": "https://www.nature.com/articles/s41598-026-62329-1_reference.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-07-01",
  "publisher": "Nature Portfolio",
  "ris": "https://research.draconitic.com/paper/2ctwnthgpqp58.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T13:45:58Z",
      "native_id": "a8d8b28340ca40b8b3f9877a0d4a89de",
      "source": "doaj",
      "url": "https://doi.org/10.1038/s41598-026-62329-1"
    }
  ],
  "subjects": [
    {
      "field": "medicine",
      "origin": "lcc:R",
      "subfield": "general"
    }
  ],
  "title": "Dental arch classification using deep learning and federated learning on DenPAR radiographs",
  "title_text": "Dental arch classification using deep learning and federated learning on DenPAR radiographs",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T13:45:58Z",
  "url": "https://research.draconitic.com/paper/2ctwnthgpqp58",
  "venue": "Scientific Reports",
  "versions": []
}