Fused Deep Features Based Classıfıcatıon Framework For Covıd-19 Classıfıcatıon Wİth Optimized Mlp

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dc.contributor.authorÖztürk, Şaban
dc.contributor.authorYiğit, Enes
dc.contributor.authorÖzkaya, Umut
dc.date.accessioned2025-01-12T17:13:32Z
dc.date.available2025-01-12T17:13:32Z
dc.date.issued2020
dc.departmentKMÜ, Mühendislik ve Mimarlık Fakültesi, Elektirik Elektronik Mühendisliği Bölümü
dc.description.abstractThe new type of Coronavirus disease called COVID-19 continues to spread quite rapidly. Although it shows some specific symptoms, this disease, which can show different symptoms in almost every individual, has caused hundreds of thousands of patients to die. Although healthcare professionals work hard to prevent further loss of life, the rate of disease spread is very high. For this reason, the help of computer aided diagnosis (CAD) and artificial intelligence (AI) algorithms is vital. In this study, a method based on optimization of convolutional neural network (CNN) architecture, which is the most effective image analysis method of today, is proposed to fulfill the mentioned COVID-19 detection needs. First, COVID-19 images are trained using ResNet-50 and VGG-16 architectures. Then, features in the last layer of these two architectures are combined with feature fusion. These new image features matrices obtained with feature fusion are classified for COVID detection. A multi-layer perceptron (MLP) structure optimized by the whale optimization algorithm is used for the classification process. The obtained results show that the performance of the proposed framework is almost 4.5% higher than VGG-16 performance and almost 3.5% higher than ResNet-50 performance.
dc.identifier.doi10.36306/konjes.821782
dc.identifier.endpage27
dc.identifier.issn2667-8055
dc.identifier.issue0
dc.identifier.startpage15
dc.identifier.trdizinid491225
dc.identifier.urihttps://doi.org/10.36306/konjes.821782
dc.identifier.urihttps://search.trdizin.gov.tr/tr/yayin/detay/491225
dc.identifier.volume8
dc.indekslendigikaynakTR-Dizin
dc.institutionauthoridÖzkaya, Umut/0000-0002-9244-0024
dc.language.isoen
dc.relation.ispartofKonya mühendislik bilimleri dergisi (Online)
dc.relation.publicationcategoryMakale - Ulusal Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_20250111
dc.subjectTıbbi İnformatik
dc.subjectBilgisayar Bilimleri
dc.subjectYazılım Mühendisliği
dc.subjectViroloji
dc.titleFused Deep Features Based Classıfıcatıon Framework For Covıd-19 Classıfıcatıon Wİth Optimized Mlp
dc.typeArticle

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