Thyroid nodule classification in ultrasound imaging using deep transfer learning

Our findings demonstrate that the fusion model, which integrates a convolutional neural network (CNN) with traditional machine learning and deep transfer learning techniques, can effectively differentiate between benign and malignant thyroid nodules through the analysis of ultrasound images. This model fusion approach significantly optimizes and enhances diagnostic performance, offering a robust and intelligent tool for the clinical detection of thyroid diseases.

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Publication Details

Journal
BMC Cancer
Published
2025-03-25
DOI
https://doi.org/10.1186/s12885-025-13917-3
Citations
7
Primary Topic
Radiomics and Machine Learning in Medical Imaging
Type
article
Field-Weighted Citation Impact
9.52
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article

Thyroid nodule classification in ultrasound imaging using deep transfer learning

Zhe Geng, Yan Xu, Bin Meng, Mingmin Xu et al.
7 citations
BMC Cancer
Radiomics and Machine Learning in Medical Imaging
9.52
article

Thyroid nodule classification in ultrasound imaging using deep transfer learning

Zhe Geng, Yan Xu, Bin Meng, Mingmin Xu, Jie Liu
article en
7 citations

Abstract

Our findings demonstrate that the fusion model, which integrates a convolutional neural network (CNN) with traditional machine learning and deep transfer learning techniques, can effectively differentiate between benign and malignant thyroid nodules through the analysis of ultrasound images. This model fusion approach significantly optimizes and enhances diagnostic performance, offering a robust and intelligent tool for the clinical detection of thyroid diseases.

BMC CancerVol. 25(1)
Shanghai University of Traditional Chinese Medicine (CN), First Hospital of Jiaxing (CN)
Openalex Percentile: Top 2%
Radiomics and Machine Learning in Medical Imaging
9.52
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