COVID19 Classification Using CT Images via Ensembles of Deep Learning Models
DC Field | Value | Language |
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dc.contributor.author | Majid, Abdul | - |
dc.contributor.author | Khan, Muhammad Attique | - |
dc.contributor.author | Nam, Yunyoung | - |
dc.contributor.author | Tariq, Usman | - |
dc.contributor.author | Roy, Sudipta | - |
dc.contributor.author | Mostafa, Reham R. | - |
dc.contributor.author | Sakr, Rasha H. | - |
dc.date.accessioned | 2021-09-10T06:27:14Z | - |
dc.date.available | 2021-09-10T06:27:14Z | - |
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/19090 | - |
dc.description.abstract | The recent COVID-19 pandemic caused by the novel coronavirus, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has had a significant impact on human life and the economy around the world. A reverse transcription polymerase chain reaction (RT-PCR) test is used to screen for this disease, but its low sensitivity means that it is not sufficient for early detection and treatment. As RT-PCR is a time-consuming procedure, there is interest in the introduction of automated techniques for diagnosis. Deep learning has a key role to play in the field of medical imaging. The most important issue in this area is the choice of key features. Here, we propose a set of deep learning features based on a system for automated classification of computed tomography (CT) images to identify COVID-19. Initially, this method was used to prepare a database of three classes: Pneumonia, COVID-19, and Healthy. The dataset consisted of 6000 CT images refined by a hybrid contrast stretching approach. In the next step, two advanced deep learning models (ResNet50 and DarkNet53) were fine-tuned and trained through transfer learning. The features were extracted from the second last feature layer of both models and further optimized using a hybrid optimization approach. For each deep model, the Rao-1 algorithm and the PSO algorithm were combined in the hybrid approach. Later, the selected features were merged using the new minimum parallel distance non-redundant (PMDNR) approach. The final fused vector was finally classified using the extreme machine classifier. The experimental process was carried out on a set of prepared data with an overall accuracy of 95.6%. Comparing the different classification algorithms at the different levels of the features demonstrated the reliability of the proposed framework. | - |
dc.format.extent | 19 | - |
dc.language | 영어 | - |
dc.language.iso | ENG | - |
dc.publisher | Tech Science Press | - |
dc.title | COVID19 Classification Using CT Images via Ensembles of Deep Learning Models | - |
dc.type | Article | - |
dc.publisher.location | 미국 | - |
dc.identifier.doi | 10.32604/cmc.2021.016816 | - |
dc.identifier.scopusid | 2-s2.0-85107842147 | - |
dc.identifier.wosid | 000659131200024 | - |
dc.identifier.bibliographicCitation | Computers, Materials and Continua, v.69, no.1, pp 319 - 337 | - |
dc.citation.title | Computers, Materials and Continua | - |
dc.citation.volume | 69 | - |
dc.citation.number | 1 | - |
dc.citation.startPage | 319 | - |
dc.citation.endPage | 337 | - |
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 | FEATURE-SELECTION | - |
dc.subject.keywordPlus | FRAMEWORK | - |
dc.subject.keywordAuthor | COVID19 | - |
dc.subject.keywordAuthor | preprocessing | - |
dc.subject.keywordAuthor | deep learning | - |
dc.subject.keywordAuthor | information fusion | - |
dc.subject.keywordAuthor | firefly algorithm | - |
dc.subject.keywordAuthor | extreme learning machine | - |
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