Classication of COVID-19 CT Scans via Extreme Learning Machin
作者机构：Department of Computer ScienceHITEC UniversityTaxila47040Pakistan Department of Electrical EngineeringCOMSATS University IslamabadWah CampusWah CanttPakistan Department of Computer Science and EngineeringSoonchunhyang UniversityAsanKorea Department of Mathematics and Computer ScienceFaculty of ScienceBeirut Arab UniversityLebanon Department of MathematicsUniversity of LeicesterLeicesterUK College of Computer Science and EngineeringUniversity of Ha’ilHa’ilSaudi Arabia
出 版 物：《Computers, Materials & Continua》 (计算机、材料和连续体（英文）)
年 卷 期：2021年第68卷第7期
页 面：1003-1019页
核心收录：
学科分类：0711[理学-系统科学] 07[理学] 08[工学] 081101[工学-控制理论与控制工程] 0811[工学-控制科学与工程] 071102[理学-系统分析与集成] 081103[工学-系统工程]
基 金：supported by Korea Institute for Advancement of Technology(KIAT)grant funded by the Korea Government(MOTIE)(P0012724,The Competency Development Program for Industry Specialist) the Soonchunhyang University Research Fun
主 题：Coronavirus classical features feature fusion feature optimization prediction
摘 要：Here,we use multi-type feature fusion and selection to predict COVID-19 infections on chest computed tomography(CT)*** scheme operates in four ***,we prepared a database containing COVID-19 pneumonia and normal CT *** images were retrieved from the Radiopaedia COVID-19 *** images were divided into training and test sets in a ratio of 70:***,multiple features were extracted from the training *** used canonical correlation analysis to fuse the features into single vectors;this enhanced the predictive *** next implemented a genetic algorithm(GA)in which an Extreme Learning Machine(ELM)served to assess GA *** on the ELM losses,the most discriminatory features were selected and saved as an ELM *** images were sent to the model,and the best-selected features compared to those of the trained model to allow nal *** employed the collected chest CT *** best predictive accuracy of the ELM classier was 93.9%;the scheme was effective.
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