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With the increasing use of enhanced CT scans, a new opportunity for early thyroid cancer screening has emerged. However, existing CT-based models face challenges due to limited datasets, small sample sizes, and high noise.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>To address these challenges, we collected enhanced CT scan image data from 240 patients in Guangdong and Xinjiang, China, and established a CT dataset for early thyroid cancer screening. We propose a deep learning model, the DVT model, which combines transformer DNN and transfer learning techniques to integrate time series data and address small sample sizes and high noise.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>The experimental results show that the DVT model achieves a prediction accuracy of 0.96, AUROC of 0.97, specificity of 1, and sensitivity of 0.94. These results indicate that the DVT model is a highly effective tool for early thyroid cancer screening.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>The DVT model has the potential to assist clinicians in identifying potential thyroid cancer patients and reducing patient expenses. Our study provides a new approach to thyroid cancer screening using enhanced CT scans and demonstrates the effectiveness of deep learning techniques in addressing the challenges associated with CT-based models.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1177\/15330338251323168","type":"journal-article","created":{"date-parts":[[2025,4,1]],"date-time":"2025-04-01T12:17:54Z","timestamp":1743509874000},"update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":3,"title":["An Early Thyroid Screening Model Based on Transformer and Secondary Transfer Learning for Chest and Thyroid CT Images"],"prefix":"10.1177","volume":"24","author":[{"ORCID":"https:\/\/orcid.org\/0009-0003-9740-2689","authenticated-orcid":false,"given":"Na","family":"Han","sequence":"first","affiliation":[{"name":"Macao Polytechnic University"},{"name":"Business School, Beijing Institute of Technology, Zhuhai, P. R. 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