COVIDiagnosis-Net: Deep Bayes-SqueezeNet based diagnosis of the coronavirus disease 2019 (COVID-19) from X-ray images

dc.authorid0000-0002-5159-0659en_US
dc.contributor.authorUçar, Ferhat
dc.contributor.authorKorkmaz, Deniz
dc.date.accessioned2021-06-03T07:56:03Z
dc.date.available2021-06-03T07:56:03Z
dc.date.issued2020en_US
dc.departmentMTÖ Üniversitesi, Mühendislik ve Doğa Bilimleri Fakültesi, Elektrik-Elektronik Mühendisliği Bölümüen_US
dc.description.abstractThe Coronavirus Disease 2019 (COVID-19) outbreak has a tremendous impact on global health and the daily life of people still living in more than two hundred countries. The crucial action to gain the force in the fight of COVID-19 is to have powerful monitoring of the site forming infected patients. Most of the initial tests rely on detecting the genetic material of the coronavirus, and they have a poor detection rate with the time-consuming operation. In the ongoing process, radiological imaging is also preferred where chest X-rays are highlighted in the diagnosis. Early studies express the patients with an abnormality in chest X-rays pointing to the presence of the COVID-19. On this motivation, there are several studies cover the deep learning-based solutions to detect the COVID-19 using chest X-rays. A part of the existing studies use non-public datasets, others perform on complicated Artificial Intelligent (AI) structures. In our study, we demonstrate an AI-based structure to outperform the existing studies. The SqueezeNet that comes forward with its light network design is tuned for the COVID-19 diagnosis with Bayesian optimization additive. Fine-tuned hyperparameters and augmented dataset make the proposed network perform much better than existing network designs and to obtain a higher COVID-19 diagnosis accuracy.en_US
dc.identifier.citationUcar, F., & Korkmaz, D. (July 01, 2020). COVIDiagnosis-Net: Deep Bayes-SqueezeNet based diagnosis of the coronavirus disease 2019 (COVID-19) from X-ray images. Medical Hypotheses, 140.en_US
dc.identifier.doi10.1016/j.mehy.2020.109761
dc.identifier.endpage12en_US
dc.identifier.issn0306-9877en_US
dc.identifier.issn1532-2777en_US
dc.identifier.pmid32344309
dc.identifier.scopus2-s2.0-85083721797en_US
dc.identifier.scopusqualityQ2en_US
dc.identifier.startpage1en_US
dc.identifier.urihttps://doi.org/10.1016/j.mehy.2020.109761
dc.identifier.urihttps://hdl.handle.net/20.500.12899/178
dc.identifier.volume140en_US
dc.identifier.wosWOS:000540720800018en_US
dc.identifier.wosqualityQ4en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.indekslendigikaynakPubMeden_US
dc.institutionauthorKorkmaz, Deniz
dc.language.isoenen_US
dc.publisherElsevieren_US
dc.relation.ispartofMedical Hypothesesen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectCoronavirus Disease 2019en_US
dc.subjectSARS-CoV-2en_US
dc.subjectRapid Diagnosis of COVID-19en_US
dc.subjectDeep learningen_US
dc.subjectBayesian optimizationen_US
dc.titleCOVIDiagnosis-Net: Deep Bayes-SqueezeNet based diagnosis of the coronavirus disease 2019 (COVID-19) from X-ray imagesen_US
dc.typeArticleen_US

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