International Journal of Cancer Management
The Official Journal of Cancer Research Center (CRC), Shahid Beheshti University of Medical Sciences
Scopus by Title (Ref)
1. A study on ultrasound imaging for thyroid detection and classification using machine learning and deep learning techniques
- Sathya J. , et al.
2. Automatic Classification of Thyroid Nodules in Ultrasound Images using MobileNetV2-MSVM Hybrid Model
- Luong D.T. , et al.
3. CT-based texture analysis predicts BRAFV600E mutation in calcified papillary thyroid carcinoma
- Chen Y. , et al.
4. CT-based explainable machine learning for predicting benign and malignant thyroid nodules: a multi-center study
- He H. , et al.
5. Differential diagnosis of thyroid nodules using heterogeneity quantification software on ultrasound images: correlation with the Bethesda system and surgical pathology
- Ryu Y.J. , et al.
6. A systematic review of machine learning based thyroid tumor characterisation using ultrasonographic images
- Yadav N. , et al.
7. The Application of Artificial Intelligence in Thyroid Nodules: A Systematic Review Based on Bibliometric Analysis
- Peng Y. , et al.
8. A contemporary review on soft computing techniques for thyroid identification and detection
- Srivastava R. , et al.
9. Artificial intelligence in thyroid ultrasound
- Cao C.L. , et al.
10. Radiomic Detection of Malignancy within Thyroid Nodules Using Ultrasonography—A Systematic Review and Meta-Analysis
- Cleere E.F. , et al.
11. Texture and shape analysis of diffusion-weighted imaging for thyroid nodules classification using machine learning
- Sharafeldeen A. , et al.
12. Automated Breast Volume Scanner (ABVS)-Based Radiomic Nomogram: A Potential Tool for Reducing Unnecessary Biopsies of BI-RADS 4 Lesions
- Wang S.J. , et al.
13. Computer Techniques for Medical Image Classification: A Review
- Adebisi O.A. , et al.
14. Texture Analysis of Liver Ultrasound Images
- Yadav N. , et al.
15. A hybrid model for the identification and classification of thyroid nodules in medical ultrasound images
- Srivastava R. , et al.
16. Value of Quantitative CTTA in Differentiating Malignant from Benign Bosniak III Renal Lesions on CT Images
- Zhang Y. , et al.
17. Radiomics score combined with acr ti-rads in discriminating benign and malignant thyroid nodules based on ultrasound images: A retrospective study
- Luo P. , et al.
18. Ultrasound image classification of thyroid nodules using machine learning techniques
- Vadhiraj V.V. , et al.
19. Feasibility of the quantitative assessment method for ct quality control in phantom image evaluation
- Lee K.B. , et al.
20. CT texture analysis in differentiating benign and malignant thyroid nodules and predicting lymph node metastasis
- Chen J. , et al.
22. The diagnostic efficiency of ultrasound computer–aided diagnosis in differentiating thyroid nodules: A systematic review and narrative synthesis
- Chambara N. , et al.
23. Update on thyroid ultrasound: A narrative review from diagnostic criteria to artificial intelligence techniques
- Liang X.W. , et al.
24. CAD system based on B-mode and color Doppler sonographic features may predict if a thyroid nodule is hot or cold
- Abbasian Ardakani A. , et al.