A novel medical diagnosis model for covid-19 infection detection based on deep features and bayesian optimization

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dc.authorid0000-0001-5256-7648en_US
dc.contributor.authorNour, Majid
dc.contributor.authorCömert, Zafer
dc.contributor.authorPolat, Kemal
dc.date.accessioned2021-06-23T19:53:48Z
dc.date.available2021-06-23T19:53:48Z
dc.date.issued2020
dc.departmentBAİBÜ, Mühendislik Fakültesi, Elektrik Elektronik Mühendisliği Bölümüen_US
dc.description.abstractA pneumonia of unknown causes, which was detected in Wuhan, China, and spread rapidly throughout the world, was declared as Coronavirus disease 2019 (COVID-19). Thousands of people have lost their lives to this disease. Its negative effects on public health are ongoing. In this study, an intelligence computer-aided model that can automatically detect positive COVID-19 cases is proposed to support daily clinical applications. The proposed model is based on the convolution neural network (CNN) architecture and can automatically reveal discriminative features on chest X-ray images through its convolution with rich filter families, abstraction, and weight-sharing characteristics. Contrary to the generally used transfer learning approach, the proposed deep CNN model was trained from scratch. Instead of the pre-trained CNNs, a novel serial network consisting of five convolution layers was designed. This CNN model was utilized as a deep feature extractor. The extracted deep discriminative features were used to feed the machine learning algorithms, which were k-nearest neighbor, support vector machine (SVM), and decision tree. The hyperparameters of the machine learning models were optimized using the Bayesian optimization algorithm. The experiments were conducted on a public COVID-19 radiology database. The database was divided into two parts as training and test sets with 70% and 30% rates, respectively. As a result, the most efficient results were ensured by the SVM classifier with an accuracy of 98.97%, a sensitivity of 89.39%, a specificity of 99.75%, and an F-score of 96.72%. Consequently, a cheap, fast, and reliable intelligence tool has been provided for COVID-19 infection detection. The developed model can be used to assist field specialists, physicians, and radiologists in the decision-making process. Thanks to the proposed tool, the misdiagnosis rates can be reduced, and the proposed model can be used as a retrospective evaluation tool to validate positive COVID-19 infection cases. (C) 2020 Elsevier B.V. All rights reserved.en_US
dc.identifier.doi10.1016/j.asoc.2020.106580
dc.identifier.issn1568-4946
dc.identifier.issn1872-9681
dc.identifier.pmid32837453en_US
dc.identifier.scopus2-s2.0-85088961822en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.urihttps://doi.org/10.1016/j.asoc.2020.106580
dc.identifier.urihttps://hdl.handle.net/20.500.12491/10260
dc.identifier.volume97en_US
dc.identifier.wosWOS:000602870700009en_US
dc.identifier.wosqualityQ1en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.indekslendigikaynakPubMeden_US
dc.institutionauthorPolat, Kemal
dc.language.isoenen_US
dc.publisherElsevieren_US
dc.relation.ispartofApplied Soft Computingen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectCOVID-19en_US
dc.subjectMedical Decision Support Systemen_US
dc.subjectDeep Learningen_US
dc.subjectDeep Feature Extractionen_US
dc.subjectMachine Learningen_US
dc.titleA novel medical diagnosis model for covid-19 infection detection based on deep features and bayesian optimizationen_US
dc.typeArticleen_US

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