ArticleSensors (Basel, Switzerland)2020
Ultrasound Image-Based Diagnosis of Malignant Thyroid Nodule Using Artificial Intelligence.
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 42 papers.
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Who cites it
42 citing papers in PubMed, 112 citations in OpenAlex.
- Dynamic Ensemble Transfer Learning with Multi-view Ultrasonography for Improving Thyroid Cancer Diagnostic Reliability.Journal of imaging informatics in medicine · 2026Article
- Technical challenges in autonomous robotic ultrasound examinations: perception, planning, and control.Journal of robotic surgery · 2026Review
- Improving Non-Invasive Prediction of Thyroid Nodule Malignancy: A Machine Learning-Based Clinical Approach.Cancer management and research · 2026Article
- Pathological diagnosis of thyroid nodules directly from ultrasonography by a weakly supervised deep learning framework.Frontiers in endocrinology · 2026Article
- A novel hybrid deep learning and chaotic dynamics approach for thyroid cancer classification.Scientific reports · 2025Article
- A novel hybrid deep learning approach combining deep feature attention and statistical validation for enhanced thyroid ultrasound segmentation.Scientific reports · 2025Article
- Evaluating the impact of sex bias on AI models in musculoskeletal ultrasound of joint recess distension.PloS one · 2025Article
- Advancing thyroid diagnosis: integrating AI-driven CAD framework with numerical data and ultrasound images.PeerJ. Computer science · 2025Article
- A predictive nomogram of thyroid nodules based on deep learning ultrasound image analysis.Frontiers in endocrinology · 2025Article
- Intranodular and perinodular ultrasound radiomics distinguishes benign and malignant thyroid nodules: a multicenter study.Gland surgery · 2024Article
- Automatic Classification of Nodules from 2D Ultrasound Images Using Deep Learning Networks.Journal of imaging · 2024Article
- Diagnostic value of colour Doppler ultrasound in differentiating malignant and benign nodules in thyroiditis background.Journal of medical radiation sciences · 2024Article
- Artificial intelligence assisted diagnosis of early tc markers and its application.Discover oncology · 2024Review
- Deep learning to assist composition classification and thyroid solid nodule diagnosis: a multicenter diagnostic study.European radiology · 2024Article
- 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
- Comparative performance analysis of binary variants of FOX optimization algorithm with half-quadratic ensemble ranking method for thyroid cancer detection.Scientific reports · 2023Article
- Diagnostic value of a computer-assisted diagnosis system for the ultrasound features in thyroid nodules.Archives of endocrinology and metabolism · 2023Article
- A Novel Deep-Learning-Based CADx Architecture for Classification of Thyroid Nodules Using Ultrasound Images.Interdisciplinary sciences, computational life sciences · 2023Article
- A Framework for Detecting Thyroid Cancer from Ultrasound and Histopathological Images Using Deep Learning, Meta-Heuristics, and MCDM Algorithms.Journal of imaging · 2023Article
- A Deep Learning Framework for the Characterization of Thyroid Nodules from Ultrasound Images Using Improved Inception Network and Multi-Level Transfer Learning.Diagnostics (Basel, Switzerland) · 2023Article
Corrections and comments
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Authors and funding
5 authors at 1 institution in 1 country.
Funding
Abstract
Computer-aided diagnosis systems have been developed to assist doctors in diagnosing thyroid nodules to reduce errors made by traditional diagnosis methods, which are mainly based on the experiences of doctors. Therefore, the performance of such systems plays an important role in enhancing the quality of a diagnosing task. Although there have been the state-of-the art studies regarding this problem, which are based on handcrafted features, deep features, or the combination of the two, their performances are still limited. To overcome these problems, we propose an ultrasound image-based diagnosis of the malignant thyroid nodule method using artificial intelligence based on the analysis in both spatial and frequency domains. Additionally, we propose the use of weighted binary cross-entropy loss function for the training of deep convolutional neural networks to reduce the effects of unbalanced training samples of the target classes in the training data. Through our experiments with a popular open dataset, namely the thyroid digital image database (TDID), we confirm the superiority of our method compared to the state-of-the-art methods.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.