Deep Learning based Classification of Thyroid Cancer using Different Medical Imaging Modalities : A Systematic Review

Author:

Ilyas Maheen,Malik Hassaan,Adnan Muhammad,Bashir Umair,Bukhari Wajahat Anwaar,Khan Muhammad Imran Ali,Ahmad Adnan

Abstract

Deep learning algorithms have achieved a tremendous triumph in task-specific feature classification. Deep learning methods are very much effective when a large amount of training data is scarce. It has been significantly applied for disease classification from medical imaging. The paper aims to identify and summarize the scenario of current research on thyroid cancer using deep learning methods through different medical imaging modalities which are found at present so that reseachers become capable to select a useful and the most relevant approach which might be fruitful in dealing with thyroid cancer. This may also raise a need for more work out while dealing with future challenges. This Systematic literature review (SLR) has been presented by reviewing research articles published in well-reputed venues between 2017 to 2021. A comprehensive review was performed to appraise the deep learning approaches that have been applied in classifying a thyroid nodule disorder from different medical imaging modalities. The analysis is performed based on different parameters reported in selected research studies which include classification accuracy, true-positive (TP), false-positive (FP), true-negative (TN), false-negative (FN) sensitivity, specificity, and the area under the receiver operating characteristic curve (AUROC). A total of 2,149 research studies have been obtained by applying search queries in different journals’ databases, out of them 40 papers have been selected for this SLR. Among them 22 studies have contributed sufficiently to the construction of the evaluation table which enabled the test process of methods of deep learning, having sensitivity varies between 75% to 100% (mean 89.50%) and specificity ranged from 64% to 100% (mean 84.4 %). The outputs revealed that the Convolutional Neural Network (CNN) has produced significant accuracy and has been extensively applied in the diagnosis of thyroid cancer by medical professionals. Furthermore, it is concluded that if the thyroid cancer exposure is inappropriate then it may restrict the deep learning mechanism and make its reliability challenge able.

Publisher

VFAST Research Platform

Cited by 2 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3