Deep learning based fusion model for COVID-19 diagnosis and classification using computed tomography images

4区 工程技术 Q2 Engineering
R.T.Subhalakshmi, S. Balamurugan, S. Sasikala
{"title":"Deep learning based fusion model for COVID-19 diagnosis and classification using computed tomography images","authors":"R.T.Subhalakshmi, S. Balamurugan, S. Sasikala","doi":"10.1177/1063293X211021435","DOIUrl":null,"url":null,"abstract":"Recently, the COVID-19 pandemic becomes increased in a drastic way, with the availability of a limited quantity of rapid testing kits. Therefore, automated COVID-19 diagnosis models are essential to identify the existence of disease from radiological images. Earlier studies have focused on the development of Artificial Intelligence (AI) techniques using X-ray images on COVID-19 diagnosis. This paper aims to develop a Deep Learning Based MultiModal Fusion technique called DLMMF for COVID-19 diagnosis and classification from Computed Tomography (CT) images. The proposed DLMMF model operates on three main processes namely Weiner Filtering (WF) based pre-processing, feature extraction and classification. The proposed model incorporates the fusion of deep features using VGG16 and Inception v4 models. Finally, Gaussian Naïve Bayes (GNB) based classifier is applied for identifying and classifying the test CT images into distinct class labels. The experimental validation of the DLMMF model takes place using open-source COVID-CT dataset, which comprises a total of 760 CT images. The experimental outcome defined the superior performance with the maximum sensitivity of 96.53%, specificity of 95.81%, accuracy of 96.81% and F-score of 96.73%.","PeriodicalId":55213,"journal":{"name":"Concurrent Engineering-Research and Applications","volume":"7 1","pages":"116 - 127"},"PeriodicalIF":0.0000,"publicationDate":"2021-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"8","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Concurrent Engineering-Research and Applications","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.1177/1063293X211021435","RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"Engineering","Score":null,"Total":0}
引用次数: 8

Abstract

Recently, the COVID-19 pandemic becomes increased in a drastic way, with the availability of a limited quantity of rapid testing kits. Therefore, automated COVID-19 diagnosis models are essential to identify the existence of disease from radiological images. Earlier studies have focused on the development of Artificial Intelligence (AI) techniques using X-ray images on COVID-19 diagnosis. This paper aims to develop a Deep Learning Based MultiModal Fusion technique called DLMMF for COVID-19 diagnosis and classification from Computed Tomography (CT) images. The proposed DLMMF model operates on three main processes namely Weiner Filtering (WF) based pre-processing, feature extraction and classification. The proposed model incorporates the fusion of deep features using VGG16 and Inception v4 models. Finally, Gaussian Naïve Bayes (GNB) based classifier is applied for identifying and classifying the test CT images into distinct class labels. The experimental validation of the DLMMF model takes place using open-source COVID-CT dataset, which comprises a total of 760 CT images. The experimental outcome defined the superior performance with the maximum sensitivity of 96.53%, specificity of 95.81%, accuracy of 96.81% and F-score of 96.73%.
基于深度学习的计算机断层图像诊断与分类融合模型
最近,COVID-19大流行疫情急剧加剧,而快速检测试剂盒的数量有限。因此,自动COVID-19诊断模型对于从放射图像中识别疾病的存在至关重要。此前的研究重点是开发利用x射线图像诊断新冠肺炎的人工智能技术。本文旨在开发一种基于深度学习的多模态融合技术,称为DLMMF,用于从计算机断层扫描(CT)图像中诊断和分类COVID-19。所提出的DLMMF模型主要包括三个过程:基于Weiner滤波(WF)的预处理、特征提取和分类。该模型使用VGG16和Inception v4模型融合了深度特征。最后,采用基于高斯Naïve贝叶斯(GNB)的分类器对测试CT图像进行识别和分类。DLMMF模型使用开源COVID-CT数据集进行实验验证,该数据集共包含760张CT图像。实验结果表明,该方法的最大灵敏度为96.53%,特异性为95.81%,准确度为96.81%,f评分为96.73%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
CiteScore
6.40
自引率
0.00%
发文量
39
审稿时长
5.3 months
期刊介绍: Original articles provide current information that help tailor foamed plastics to specific product and market requirements. Diagrams, flowcharts and photographs illustrate new processing steps and machinery. This journal is a member of the Committee on Publication Ethics (COPE).
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信