A Review on Ultrasound-Based Thyroid Cancer Tissue Characterization and Automated Classification

Author:

Acharya U. Rajendra12,Swapna G.3,Sree S. Vinitha4,Molinari Filippo5,Gupta Savita6,Bardales Ricardo H.7,Witkowska Agnieszka8,Suri Jasjit S.9

Affiliation:

1. Department of Electronics and Communication Engineering, Ngee Ann Polytechnic, Singapore 599489

2. Department of Biomedical Engineering, Faculty of Engineering, University of Malaya, 50603 Kuala Lumpur, Malaysia

3. Department of Applied Electronics and Instrumentation, Government Engineering College, Kozhikode, Kerala 673005, India

4. Global Biomedical Technologies Inc., CA, USA

5. Biolab, Department of Electronics and Telecommunications, Politecnico di Torino, Torino, Italy

6. Department of Computer Science and Engineering, University Institute of Engineering and Technology (UIET), Panjab University, Chandigarh, India

7. Outpatient Pathology Associates, Sacramento, CA 95816

8. Department of Internal Medicine, Diabetology and Nephrology, Medical University of Silesia, Zabrze, Poland

9. ThyroScan Division, Global Biomedical Technologies, Inc., CA, USA; AtheroPoint(TM), LLC, Roseville, CA, USA; Electrical Engineering Department, Idaho State University (Affl.), ID, USA

Abstract

In this paper, we review the different studies that developed Computer Aided Diagnostic (CAD) for automated classification of thyroid cancer into benign and malignant types. Specifically, we discuss the different types of features that are used to study and analyze the differences between benign and malignant thyroid nodules. These features can be broadly categorized into (a) the sonographic features from the ultrasound images, and (b) the non-clinical features extracted from the ultrasound images using statistical and data mining techniques. We also present a brief description of the commonly used classifiers in ultrasound based CAD systems. We then review the studies that used features based on the ultrasound images for thyroid nodule classification and highlight the limitations of such studies. We also discuss and review the techniques used in studies that used the non-clinical features for thyroid nodule classification and report the classification accuracies obtained in these studies.

Publisher

SAGE Publications

Subject

Cancer Research,Oncology

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1. Multi-Model Deep Learning Framework for Thyroid Cancer Classification Using Ultrasound Imaging;International Conference on Information Systems Development;2024-09-09

2. CNN and ResNet50 Model Design for Improved Ultrasound Thyroid Nodules Detection;2024 ASU International Conference in Emerging Technologies for Sustainability and Intelligent Systems (ICETSIS);2024-01-28

3. Identification method of thyroid nodule ultrasonography based on self-supervised learning dual-branch attention learning framework;Health Information Science and Systems;2024-01-17

4. Deep Learning Paradigm and Its Bias for Coronary Artery Wall Segmentation in Intravascular Ultrasound Scans: A Closer Look;Journal of Cardiovascular Development and Disease;2023-12-04

5. Deep learning-based CAD system design for thyroid tumor characterization using ultrasound images;Multimedia Tools and Applications;2023-10-16

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