ArticleJournal of clinical medicine2019
Artificial Intelligence-Based Thyroid Nodule Classification Using Information from Spatial and Frequency Domains.
Article in Journal of clinical medicine, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers.
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Who cites it
28 citing papers in PubMed, 90 citations in OpenAlex.
- Dynamic Ensemble Transfer Learning with Multi-view Ultrasonography for Improving Thyroid Cancer Diagnostic Reliability.Journal of imaging informatics in medicine · 2026Article
- Research progress on artificial intelligence technology-assisted diagnosis of thyroid diseases.Frontiers in oncology · 2025Review
- Ultrasound-based nomogram to predict the recurrence in papillary thyroid carcinoma using machine learning.BMC cancer · 2024Article
- A segmentation-based algorithm for classification of benign and malignancy Thyroid nodules with multi-feature information.Biomedical engineering letters · 2024Article
- An Efficient CNN-Based Method for Intracranial Hemorrhage Segmentation from Computerized Tomography Imaging.Journal of imaging · 2024Article
- Comparative performance analysis of binary variants of FOX optimization algorithm with half-quadratic ensemble ranking method for thyroid cancer detection.Scientific reports · 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
- Diagnostic value of a dynamic artificial intelligence ultrasonic intelligent auxiliary diagnosis system for benign and malignant thyroid nodules in patients with Hashimoto thyroiditis.Quantitative imaging in medicine and surgery · 2023Article
- Predicting the risk of inappropriate depth of endotracheal intubation in pediatric patients using machine learning approaches.Scientific reports · 2023Article
- Artificial intelligence in thyroid ultrasound.Frontiers in oncology · 2023Review
- AI-Assisted Ultrasound for the Early Diagnosis of Antibody-Negative Autoimmune Thyroiditis.Journal of multidisciplinary healthcare · 2023Review
- Is the diagnostic model based on convolutional neural network superior to pediatric radiologists in the ultrasonic diagnosis of biliary atresia?Frontiers in medicine · 2023Article
- Review
- Radiomic Detection of Malignancy within Thyroid Nodules Using Ultrasonography-A Systematic Review and Meta-Analysis.Diagnostics (Basel, Switzerland) · 2022Review
- Identification of benign and malignant thyroid nodules based on dynamic AI ultrasound intelligent auxiliary diagnosis system.Frontiers in endocrinology · 2022Article
- Magnetic Resonance Imaging under Image Enhancement Algorithm to Analyze the Clinical Value of Placement of Drainage Tube on Incision Healing after Hepatobiliary Surgery.Computational and mathematical methods in medicine · 2022Article
- Artificial intelligence to detect the femoral intertrochanteric fracture: The arrival of the intelligent-medicine era.Frontiers in bioengineering and biotechnology · 2022Article
- Artificial Intelligence to Diagnose Tibial Plateau Fractures: An Intelligent Assistant for Orthopedic Physicians.Current medical science · 2021Article
- Application of Artificial Intelligence in Medicine: An Overview.Current medical science · 2021Review
Corrections and comments
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Authors and funding
5 authors at 1 institution in 1 country.
Funding
Abstract
Image-based computer-aided diagnosis (CAD) systems have been developed to assist doctors in the diagnosis of thyroid cancer using ultrasound thyroid images. However, the performance of these systems is strongly dependent on the selection of detection and classification methods. Although there are previous researches on this topic, there is still room for enhancement of the classification accuracy of the existing methods. To address this issue, we propose an artificial intelligence-based method for enhancing the performance of the thyroid nodule classification system. Thus, we extract image features from ultrasound thyroid images in two domains: spatial domain based on deep learning, and frequency domain based on Fast Fourier transform (FFT). Using the extracted features, we perform a cascade classifier scheme for classifying the input thyroid images into either benign (negative) or malign (positive) cases. Through expensive experiments using a public dataset, the thyroid digital image database (TDID) dataset, we show that our proposed method outperforms the state-of-the-art methods and produces up-to-date classification results for the thyroid nodule classification problem.
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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.