Classification of Positive COVID-19 CT Scans Using Deep Learning
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Khan, Muhammad Attique | - |
dc.contributor.author | Hussain, Nazar | - |
dc.contributor.author | Majid, Abdul | - |
dc.contributor.author | Alhaisoni, Majed | - |
dc.contributor.author | Bukhari, Syed Ahmad Chan | - |
dc.contributor.author | Kadry, Seifedine | - |
dc.contributor.author | Nam, Yunyoung | - |
dc.contributor.author | Zhang, Yu-Dong | - |
dc.date.accessioned | 2021-08-11T08:31:14Z | - |
dc.date.available | 2021-08-11T08:31:14Z | - |
dc.date.issued | 2021 | - |
dc.identifier.issn | 1546-2218 | - |
dc.identifier.issn | 1546-2226 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/sch/handle/2021.sw.sch/2210 | - |
dc.description.abstract | In medical imaging, computer vision researchers are faced with a variety of features for verifying the authenticity of classifiers for an accurate diagnosis. In response to the coronavirus 2019 (COVID-19) pandemic, new testing procedures, medical treatments, and vaccines are being developed rapidly. One potential diagnostic tool is a reverse-transcription polymerase chain reaction (RT-PCR). RT-PCR, typically a time-consuming process, was less sensitive to COVID-19 recognition in the disease's early stages. Here we introduce an optimized deep learning (DL) scheme to distinguish COVID-19-infected patients from normal patients according to computed tomography (CT) scans. In the proposed method, contrast enhancement is used to improve the quality of the original images. A pretrained DenseNet-201 DL model is then trained using transfer learning. Two fully connected layers and an average pool are used for feature extraction. The extracted deep features are then optimized with a Firefly algorithm to select the most optimal learning features. Fusing the selected features is important to improving the accuracy of the approach; however, it directly affects the computational cost of the technique. In the proposed method, a new parallel high index technique is used to fuse two optimal vectors; the outcome is then passed on to an extreme learning machine for final classification. Experiments were conducted on a collected database of patients using a 70:30 training: Testing ratio. Our results indicated an average classification accuracy of 94.76% with the proposed approach. A comparison of the outcomes to several other DL models demonstrated the effectiveness of our DL method for classifying COVID-19 based on CT scans. | - |
dc.format.extent | 16 | - |
dc.language | 영어 | - |
dc.language.iso | ENG | - |
dc.publisher | Tech Science Press | - |
dc.title | Classification of Positive COVID-19 CT Scans Using Deep Learning | - |
dc.type | Article | - |
dc.publisher.location | 미국 | - |
dc.identifier.doi | 10.32604/cmc.2021.013191 | - |
dc.identifier.scopusid | 2-s2.0-85098751242 | - |
dc.identifier.wosid | 000604616100010 | - |
dc.identifier.bibliographicCitation | Computers, Materials and Continua, v.66, no.3, pp 2923 - 2938 | - |
dc.citation.title | Computers, Materials and Continua | - |
dc.citation.volume | 66 | - |
dc.citation.number | 3 | - |
dc.citation.startPage | 2923 | - |
dc.citation.endPage | 2938 | - |
dc.type.docType | Article | - |
dc.description.isOpenAccess | N | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Computer Science | - |
dc.relation.journalResearchArea | Materials Science | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Information Systems | - |
dc.relation.journalWebOfScienceCategory | Materials Science, Multidisciplinary | - |
dc.subject.keywordPlus | MODEL | - |
dc.subject.keywordAuthor | Coronavirus | - |
dc.subject.keywordAuthor | contrast enhancement | - |
dc.subject.keywordAuthor | deep learning | - |
dc.subject.keywordAuthor | features optimization | - |
dc.subject.keywordAuthor | fusion | - |
dc.subject.keywordAuthor | classification | - |
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