COVID-19 Detection by Optimizing Deep Residual Features with Improved Clustering-Based Golden Ratio Optimizer
DC Field | Value | Language |
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dc.contributor.author | Chattopadhyay, Soham | - |
dc.contributor.author | Dey, Arijit | - |
dc.contributor.author | Singh, Pawan Kumar | - |
dc.contributor.author | Geem, Zong Woo | - |
dc.contributor.author | Sarkar, Ram | - |
dc.date.available | 2021-03-19T00:40:10Z | - |
dc.date.created | 2021-03-19 | - |
dc.date.issued | 2021-02 | - |
dc.identifier.issn | 2075-4418 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/gachon/handle/2020.sw.gachon/80470 | - |
dc.description.abstract | The COVID-19 virus is spreading across the world very rapidly. The World Health Organization (WHO) declared it a global pandemic on 11 March 2020. Early detection of this virus is necessary because of the unavailability of any specific drug. The researchers have developed different techniques for COVID-19 detection, but only a few of them have achieved satisfactory results. There are three ways for COVID-19 detection to date, those are real-time reverse transcription-polymerize chain reaction (RT-PCR), Computed Tomography (CT), and X-ray plays. In this work, we have proposed a less expensive computational model for automatic COVID-19 detection from Chest X-ray and CT-scan images. Our paper has a two-fold contribution. Initially, we have extracted deep features from the image dataset and then introduced a completely novel meta-heuristic feature selection approach, named Clustering-based Golden Ratio Optimizer (CGRO). The model has been implemented on three publicly available datasets, namely the COVID CT-dataset, SARS-Cov-2 dataset, and Chest X-Ray dataset, and attained state-of-the-art accuracies of 99.31%, 98.65%, and 99.44%, respectively. | - |
dc.language | 영어 | - |
dc.language.iso | en | - |
dc.publisher | MDPI | - |
dc.relation.isPartOf | DIAGNOSTICS | - |
dc.title | COVID-19 Detection by Optimizing Deep Residual Features with Improved Clustering-Based Golden Ratio Optimizer | - |
dc.type | Article | - |
dc.type.rims | ART | - |
dc.description.journalClass | 1 | - |
dc.identifier.wosid | 000622479700001 | - |
dc.identifier.doi | 10.3390/diagnostics11020315 | - |
dc.identifier.bibliographicCitation | DIAGNOSTICS, v.11, no.2 | - |
dc.description.isOpenAccess | N | - |
dc.identifier.scopusid | 2-s2.0-85104795474 | - |
dc.citation.title | DIAGNOSTICS | - |
dc.citation.volume | 11 | - |
dc.citation.number | 2 | - |
dc.contributor.affiliatedAuthor | Geem, Zong Woo | - |
dc.type.docType | Article | - |
dc.subject.keywordAuthor | COVID-19 detection | - |
dc.subject.keywordAuthor | CGRO algorithm | - |
dc.subject.keywordAuthor | deep features | - |
dc.subject.keywordAuthor | meta-heuristic | - |
dc.subject.keywordAuthor | feature selection | - |
dc.subject.keywordAuthor | CT-scan | - |
dc.subject.keywordAuthor | chest X-ray | - |
dc.relation.journalResearchArea | General & Internal Medicine | - |
dc.relation.journalWebOfScienceCategory | Medicine, General & Internal | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
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