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Review Papers | Computer Science | Volume 15 Issue 2, February 2026 | Pages: 1618 - 1624 | India
Advancing Thyroid Nodule Detection: A Deep Learning Driven Survey
Abstract: Thyroid nodules are a common endocrine abnormality, and early identification is essential for accurate diagnosis and timely intervention. Ultrasound imaging remains the preferred clinical tool for thyroid nodule assessment, yet manual interpretation is highly dependent on radiologist expertise and often results in variability. Deep learning techniques, particularly convolutional and transformer-based architectures, offer enhanced capabilities for automated detection, segmentation, and classification of thyroid nodules. This review provides a structured and concise survey of recent deep learning developments in thyroid ultrasound analysis, outlining key models, datasets, strengths, and limitations. The study also highlights current trends in computer-aided diagnosis and identifies future research directions aimed at improving generalizability, interpretability, and clinical adaptability for real-world deployment.
Keywords: Thyroid nodule detection, Ultrasound imaging, Deep learning, Convolutional neural networks, Computer-aided diagnosis, Medical image analysis
How to Cite?: M. Jenifer, K. P. Malarkodi, "Advancing Thyroid Nodule Detection: A Deep Learning Driven Survey", Volume 15 Issue 2, February 2026, International Journal of Science and Research (IJSR), Pages: 1618-1624, https://www.ijsr.net/getabstract.php?paperid=SR26226144258, DOI: https://dx.dx.doi.org/10.21275/SR26226144258