Deep learning network selection and optimized information fusion for enhanced COVID‐19 detection

Author:

Ali Muhammad Umair1,Zafar Amad1ORCID,Tanveer Jawad2ORCID,Khan Muhammad Attique3ORCID,Kim Seong Han1,Alsulami Mashael M.4,Lee Seung Won5

Affiliation:

1. Department of Intelligent Mechatronics Engineering Sejong University Seoul Republic of Korea

2. Department of Computer Science and Engineering Sejong University Seoul Republic of Korea

3. Department of Computer Science HITEC University Taxila Taxila Pakistan

4. Department of Information Technology, College of Computers and Information Technology Taif University Taif Saudi Arabia

5. Department of Precision Medicine Sungkyunkwan University School of Medicine Suwon Republic of Korea

Abstract

AbstractThis study proposes a wrapper‐based technique to improve the classification performance of chest infection (including COVID‐19) detection using X‐rays. Deep features were extracted using pretrained deep learning models. Ten optimization techniques, including poor and rich optimization, path finder algorithm, Henry gas solubility optimization, Harris hawks optimization, atom search optimization, manta‐ray foraging optimization, equilibrium optimizer, slime mold algorithm, generalized normal distribution optimization, and marine predator algorithm, were used to determine the optimal features using a support vector machine. Moreover, a network selection technique was used to select the deep learning models. An online chest infection detection X‐ray scan dataset was used to validate the proposed approach. The results suggest that the proposed wrapper‐based automatic deep learning network selection and feature optimization framework has a high classification rate of 97.7%. The comparative analysis further validates the credibility of the framework in COVID‐19 and other chest infection classifications, suggesting that the proposed approach can help doctors in clinical practice.

Funder

National Research Foundation of Korea

Publisher

Wiley

Subject

Electrical and Electronic Engineering,Computer Vision and Pattern Recognition,Software,Electronic, Optical and Magnetic Materials

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