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Cross-Domain Approach for Automated Thyroid Classification Using Diff-Quick Images

Author

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  • Thanh-Ha Do

    (Faculty of Mathematics, Mechanics and Informatics, VNU University of Science, 334 Nguyen Trai Street, Thanh Xuan District, Hanoi 100000, Vietnam
    These authors contributed equally to this work.)

  • Huy Le

    (L2TI Laboratory, University Sorbonne Paris Nord, 93430 Villetaneuse, France
    These authors contributed equally to this work.)

  • Minh-Huong Hoang Dang

    (Faculty of Mathematics, Mechanics and Informatics, VNU University of Science, 334 Nguyen Trai Street, Thanh Xuan District, Hanoi 100000, Vietnam
    These authors contributed equally to this work.)

  • Van-De Nguyen

    (The 108 Military Central Hospital, Hanoi 100000, Vietnam
    These authors contributed equally to this work.)

  • Phuc Do

    (Université de Lorraine, CNRS, CRAN, 54000 Nancy, France
    These authors contributed equally to this work.)

Abstract

Classification of thyroid images based on the Bethesda category using Diff-Quick stained images can assist in diagnosing thyroid cancer. This paper proposes a cross-domain approach that modifies the original deep learning network designed to classify X-ray images to classify stained thyroid images. Since the Diff-Quick stained images have large and high-quality sizes with tiny cells with essential characteristics that can help a doctor diagnose, resizing images is required to maintain this characteristic, which is significant. Thus, in this paper, we also research and evaluate the performance of different interpolation methods, including linear and cubic interpolation. The experiment results evaluated on a private dataset present promising results in the thyroid image classification of the proposed approach.

Suggested Citation

  • Thanh-Ha Do & Huy Le & Minh-Huong Hoang Dang & Van-De Nguyen & Phuc Do, 2025. "Cross-Domain Approach for Automated Thyroid Classification Using Diff-Quick Images," Mathematics, MDPI, vol. 13(13), pages 1-12, July.
    • Handle: RePEc:gam:jmathe:v:13:y:2025:i:13:p:2191-:d:1695024
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