Diagnosis of COVID-19 using deep CNNs and particle swarm optimization

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dc.authorscopusid57209239500
dc.authorscopusid54951017400
dc.authorscopusid56436055700
dc.authorscopusid57202924825
dc.contributor.authorGürcan, Ömer Faruk
dc.contributor.authorAtıcı, Uğur
dc.contributor.authorBiçer, Mustafa Berkan
dc.contributor.authorDoğan, Onur
dc.date.accessioned2022-02-15T16:57:48Z
dc.date.available2022-02-15T16:57:48Z
dc.date.issued2022
dc.departmentBakırçay Üniversitesien_US
dc.descriptionInternational Conference on Intelligent and Fuzzy Systems, INFUS 2021 -- 24 August 2021 through 26 August 2021 -- -- 264409en_US
dc.description.abstractCoronavirus pandemic (COVID-19) is an infectious illness. A newly explored coronavirus caused it. Currently, more than 112 million verified cases of COVID-19, containing 2,4 million deaths, are reported to WHO (February 2021). Scientists are working to develop treatments. Early detection and treatment of COVID-19 are critical to fighting disease. Recently, automated systems, specifically deep learning-based models, address the COVID-19 diagnosis task. There are various ways to test COVID-19. Imaging technologies are widely available, and chest X-ray and computed tomography images are helpful. A publicly available dataset was used in this study, including chest X-ray images of normal, COVID-19, and viral pneumonia. Firstly, images were pre-processed. Three deep learning models, namely DarkNet-53, ResNet-18, and Xception, were used in feature extraction from images. The number of extracted features was decreased by Binary Particle Swarm Optimization. Lastly, features were classified using Logistic Regression, Support Vector Machine, and XGBoost. The maximum accuracy score is 99.7% in a multi-classification task. This study reveals that pre-trained deep learning models with a metaheuristic-based feature selection give robust results. The proposed model aims to help healthcare professionals in COVID-19 diagnosis. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.en_US
dc.identifier.doi10.1007/978-3-030-85577-2_36
dc.identifier.endpage312en_US
dc.identifier.isbn9783030855765
dc.identifier.issn2367-3370
dc.identifier.scopus2-s2.0-85115275620en_US
dc.identifier.scopusqualityQ4en_US
dc.identifier.startpage305en_US
dc.identifier.urihttps://doi.org/10.1007/978-3-030-85577-2_36
dc.identifier.urihttps://hdl.handle.net/20.500.14034/278
dc.identifier.volume308en_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherSpringer Science and Business Media Deutschland GmbHen_US
dc.relation.journalLecture Notes in Networks and Systemsen_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectCOVID-19en_US
dc.subjectDeep learningen_US
dc.subjectHeuristicen_US
dc.subjectPandemicen_US
dc.subjectParticle swarm optimizationen_US
dc.titleDiagnosis of COVID-19 using deep CNNs and particle swarm optimizationen_US
dc.typeConference Objecten_US

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