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

Thanh-Ha Do (), Huy Le, Minh-Huong Hoang Dang, Nguyen Van-De and Phuc Do ()
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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
Huy Le: L2TI Laboratory, University Sorbonne Paris Nord, 93430 Villetaneuse, France
Minh-Huong Hoang Dang: Faculty of Mathematics, Mechanics and Informatics, VNU University of Science, 334 Nguyen Trai Street, Thanh Xuan District, Hanoi 100000, Vietnam
Nguyen Van-De: The 108 Military Central Hospital, Hanoi 100000, Vietnam
Phuc Do: Université de Lorraine, CNRS, CRAN, 54000 Nancy, France

Mathematics, 2025, vol. 13, issue 13, 1-12

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.

Keywords: thyroid cancer; cell classification; deep learning; cross-domain (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
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