Optimizing multi neural network weights for COVID-19 detection using enhanced artificial ecosystem algorithm

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dc.contributor.authorKoyuncu, Hakan
dc.contributor.authorArab, Munaf
dc.date.accessioned2023-10-28T08:35:39Z
dc.date.available2023-10-28T08:35:39Z
dc.date.issued2023en_US
dc.departmentEnstitüler, Lisansüstü Eğitim Enstitüsü, Elektrik ve Bilgisayar Mühendisliği Ana Bilim Dalıen_US
dc.description.abstractThe role of machine learning in medical research, particularly in addressing the COVID-19 pandemic, has proven to be significant. The current study delineates the design and refinement of an artificial intelligence (AI) framework tailored to differentiate COVID-19 from Pneumonia utilizing X-ray scans in synergy with textual clinical data. The focal point of this research is the amalgamation of diverse neural networks and the exploration of the impact of metaheuristic algorithms on optimizing these networks' weights. The proposed framework uniquely incorporates a lung segmentation process using a pre-trained ResNet34 model, generating a mask for each lung to mitigate the influence of potential extraneous features. The dataset comprised 579 segmented X-ray images (Anteroposterior and Posteroanterior views) of COVID-19 and Pneumonia patients, supplemented with each patient's textual medical data, including age and gender. An enhancement in accuracy from 94.32% to 97.85% was observed with the implementation of weight optimization in the proposed framework. The efficacy of the model in detecting COVID-19 was further ascertained through a comprehensive comparison with various architectures cited in the existing literature.en_US
dc.identifier.citationKoyuncu, H., Arab, M. (2023). Optimizing multi neural network weights for COVID-19 detection using enhanced artificial ecosystem algorithm. Traitement du Signal, 40(4), 1491-1500.en_US
dc.identifier.endpage1500en_US
dc.identifier.issn0765-0019
dc.identifier.issue4en_US
dc.identifier.scopus2-s2.0-85173249880
dc.identifier.scopusqualityN/A
dc.identifier.startpage1491en_US
dc.identifier.urihttps://hdl.handle.net/20.500.12939/4202
dc.identifier.volume40en_US
dc.identifier.wosWOS:001079705200009
dc.identifier.wosqualityQ4
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.institutionauthorKoyuncu, Hakan
dc.institutionauthorArab, Munaf
dc.language.isoen
dc.publisherInternational Information and Engineering Technology Associationen_US
dc.relation.ispartofTraitement du Signal
dc.relation.isversionof10.18280/ts.400417en_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectConvolutional Neural Networken_US
dc.subjectCOVID-19en_US
dc.subjectEnhanced Artificial Ecosystem Algorithmen_US
dc.subjectMetaheuristicsen_US
dc.subjectMultilayer Perceptronen_US
dc.subjectOptimizationen_US
dc.subjectPneumoniaen_US
dc.subjectSegmentationen_US
dc.titleOptimizing multi neural network weights for COVID-19 detection using enhanced artificial ecosystem algorithm
dc.typeArticle

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