Abstract
Ultrasound is the primary tool for thyroid nodule assessment, but diagnostic accuracy and efficiency require improvement. This study aims to develop a high-performance, computationally efficient deep learning model for thyroid nodule detection. We propose GhostYOLO, a novel framework based on YOLOv11. It incorporates a GhostDynamic Module to reduce complexity and a Multi-Scale Attention (MSAttention) mechanism to improve feature extraction across varying nodule sizes. The model was trained and evaluated on a large internal dataset of 12,385 ultrasound images from 3,140 patients and validated on an external public dataset of 8,500 images from 842 cases. On the internal test set, GhostYOLO achieved a mean Average Precision (mAP) of 67.2% and an F1-Score of 86.3%. External validation yielded a mAP50 of 74.4% (mAP: 48.9%) and an F1-Score of 79.5%. The model demonstrated high efficiency with only 9.3 million parameters and 20.2 GFLOPs. GhostYOLO offers a robust and computationally efficient solution for automated thyroid nodule detection, showing strong potential to aid in clinical diagnosis.
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We extend our thanks to all the participants and their families for their time and commitment to this research.
Funding
This work was supported by the Science and Technology Project of Shenzhen (JCYJ20230807095209018, JCYJ20210324110211031, JCYJ20210324131402008, KXCFZ202002011010487), National Key Research and Development Program of China (2023YFC3402605), the Natural Science Foundation of Guangdong Province (2022A1515010986, 2022A1515010296), the Shenzhen Key Medical Discipline Construction Fund (SZXK051), the Sanming Project of Medicine in Shenzhen (SZSM202111011), Peking University Shenzhen Hospital (LCYJZD2021010), and the Shenzhen Science and Technology Innovation Commission Key Laboratory Program (NO. ZDSYS20150430104540698).
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The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. The study was conducted in accordance with the Declaration of Helsinki. The study was approved by the Medical Ethics Committee of Peking University Shenzhen Hospital (No. [2024] 084), and the requirement of individual consent for this retrospective analysis was waived.
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This study was approved by the institutional review board, and the requirement for informed patient consent was waived due to its retrospective cohort design.
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Tan, D., Cheng, C., Zheng, T. et al. A lightweight GhostYOLO framework with GhostDynamic Module and multi-scale attention for thyroid nodule diagnosis. Sci Rep (2026). https://doi.org/10.1038/s41598-026-51141-6
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DOI: https://doi.org/10.1038/s41598-026-51141-6
