Lung disease prediction based on CT images using REInf-net and world cup optimization based BI-LSTM classification

IF 1.1 3区 计算机科学 Q4 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Padmini Sankaramurthy, Renukadevi Palaniswamy, Suseela Sellamuthu, Fancy Chelladurai, Anand Murugadhas
{"title":"Lung disease prediction based on CT images using REInf-net and world cup optimization based BI-LSTM classification","authors":"Padmini Sankaramurthy, Renukadevi Palaniswamy, Suseela Sellamuthu, Fancy Chelladurai, Anand Murugadhas","doi":"10.1080/0954898x.2024.2392782","DOIUrl":null,"url":null,"abstract":"A major global source of disability as well as mortality is respiratory illness. Though visual evaluation of computed tomography (CT) images and chest radiographs are a primary diagnostic for respi...","PeriodicalId":54735,"journal":{"name":"Network-Computation in Neural Systems","volume":"1 1","pages":"1-34"},"PeriodicalIF":1.1000,"publicationDate":"2024-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Network-Computation in Neural Systems","FirstCategoryId":"94","ListUrlMain":"https://doi.org/10.1080/0954898x.2024.2392782","RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
引用次数: 0

Abstract

A major global source of disability as well as mortality is respiratory illness. Though visual evaluation of computed tomography (CT) images and chest radiographs are a primary diagnostic for respi...
利用 REInf-net 和基于世界杯优化的 BI-LSTM 分类,基于 CT 图像预测肺部疾病
呼吸系统疾病是全球残疾和死亡的主要原因。虽然计算机断层扫描(CT)图像和胸片的目视评估是呼吸系统疾病的主要诊断方法,但它们并不是最有效的诊断方法。
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
Network-Computation in Neural Systems
Network-Computation in Neural Systems 工程技术-工程:电子与电气
CiteScore
3.70
自引率
1.30%
发文量
22
审稿时长
>12 weeks
期刊介绍: Network: Computation in Neural Systems welcomes submissions of research papers that integrate theoretical neuroscience with experimental data, emphasizing the utilization of cutting-edge technologies. We invite authors and researchers to contribute their work in the following areas: Theoretical Neuroscience: This section encompasses neural network modeling approaches that elucidate brain function. Neural Networks in Data Analysis and Pattern Recognition: We encourage submissions exploring the use of neural networks for data analysis and pattern recognition, including but not limited to image analysis and speech processing applications. Neural Networks in Control Systems: This category encompasses the utilization of neural networks in control systems, including robotics, state estimation, fault detection, and diagnosis. Analysis of Neurophysiological Data: We invite submissions focusing on the analysis of neurophysiology data obtained from experimental studies involving animals. Analysis of Experimental Data on the Human Brain: This section includes papers analyzing experimental data from studies on the human brain, utilizing imaging techniques such as MRI, fMRI, EEG, and PET. Neurobiological Foundations of Consciousness: We encourage submissions exploring the neural bases of consciousness in the brain and its simulation in machines.
×
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