ArticleSensors (Basel, Switzerland)2020
Thyroid Nodule Classification for Physician Decision Support Using Machine Learning-Evaluated Geometric and Morphological Features.
Article in Sensors (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed.
- Prediction of thyroid malignancy risk using clinical and ultrasonography features and a machine learning approach.European radiology · 2025Article
- Application of artificial intelligence in the ultrasonographic diagnosis of thyroid nodules.Journal of ultrasonography · 2025Article
- Article
- ELTIRADS framework for thyroid nodule classification integrating elastography, TIRADS, and radiomics with interpretable machine learning.Scientific reports · 2025Article
- Deep learning models for CT image classification: a comprehensive literature review.Quantitative imaging in medicine and surgery · 2025Review
- Applying machine-learning models to differentiate benign and malignant thyroid nodules classified as C-TIRADS 4 based on 2D-ultrasound combined with five contrast-enhanced ultrasound key frames.Frontiers in endocrinology · 2024Article
- Diagnostic value of a computer-assisted diagnosis system for the ultrasound features in thyroid nodules.Archives of endocrinology and metabolism · 2023Article
- Thyroid Nodule Detection and Region Estimation in Ultrasound Images: A Comparison between Physicians and an Automated Decision Support System Approach.Diagnostics (Basel, Switzerland) · 2023Article
- Artificial Intelligence in Thyroidology: A Narrative Review of the Current Applications, Associated Challenges, and Future Directions.Thyroid : official journal of the American Thyroid Association · 2023Review
- Review
- Artificial intelligence and thyroid disease management: considerations for thyroid function tests.Biochemia medica · 2022Review
- Radiomic Detection of Malignancy within Thyroid Nodules Using Ultrasonography-A Systematic Review and Meta-Analysis.Diagnostics (Basel, Switzerland) · 2022Review
- Diagnosis of Thyroid Nodules Based on Image Enhancement and Deep Neural Networks.Computational intelligence and neuroscience · 2022Article
- The value of color Doppler ultrasound in the diagnosis of thyroid nodules: a systematic review and meta-analysis.Gland surgery · 2021Article
- Ultrasound Image Classification of Thyroid Nodules Using Machine Learning Techniques.Medicina (Kaunas, Lithuania) · 2021Article
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6 authors.
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Abstract
The classification of thyroid nodules using ultrasound (US) imaging is done using the Thyroid Imaging Reporting and Data System (TIRADS) guidelines that classify nodules based on visual and textural characteristics. These are composition, shape, size, echogenicity, calcifications, margins, and vascularity. This work aims to reduce subjectivity in the current diagnostic process by using geometric and morphological (G-M) features that represent the visual characteristics of thyroid nodules to provide physicians with decision support. A total of 27 G-M features were extracted from images obtained from an open-access US thyroid nodule image database. 11 significant features in accordance with TIRADS were selected from this global feature set. Each feature was labeled (0 = benign and 1 = malignant) and the performance of the selected features was evaluated using machine learning (ML). G-M features together with ML resulted in the classification of thyroid nodules with a high accuracy, sensitivity and specificity. The results obtained here were compared against state-of the-art methods and perform significantly well in comparison. Furthermore, this method can act as a computer aided diagnostic (CAD) system for physicians by providing them with a validation of the TIRADS visual characteristics used for the classification of thyroid nodules in US images.
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