Comparative analysis of neural networks in the diagnosis of emerging diseases based on COVID-19

Basit Öğe Kaydı
dc.authorid0000-0003-4938-5207
dc.authorid0000-0002-2734-4116
dc.authorid0000-0003-3061-5770
dc.contributor.authorKirişci, Murat
dc.contributor.authorDemir, İbrahim
dc.contributor.authorŞimşek, Necip
dc.date.accessioned2024-10-12T19:47:14Z
dc.date.available2024-10-12T19:47:14Z
dc.date.issued2021
dc.departmentİstanbul Ticaret Üniversitesi, İnsan ve Toplum Bilimleri Fakültesi, Matematik Bölümüen_US
dc.description.abstractDermatological diseases are frequently encountered in children and adults for various reasons. There are many factors that cause the onset of these diseases and different symptoms are generally seen in each age group. Artificial Neural Networks can provide expert level accuracy in the diagnosis of dermatological findings of patients with COVID-19 disease. Therefore, the use of neural network classification methods can give the best estimation method in dermatology. In this study, the prediction of cutaneous diseases caused by COVID-19 was analyzed by Scaled Conjugate Gradient, Levenberg Marquardt, Bayesian Regularization neural networks. At some points, Bayesian Regularization and Levenberg Marquardt were almost equally effective, but Bayesian Regularization performed better than Levenberg Marquard and called Conjugate Gradient in performance. It is seen that neural network model predictions achieve the highest ac-curacy. For this reason, Artificial Neural Networks are able to classify these diseases as accurately as human experts in an experimental setting.en_US
dc.identifier.citationKirişci, M., Demir, İ., & Şimşek, N. (2021). Comparative Analysis of Neural Networks in the Diagnosis of Emerging Diseases Based on COVID-19. Konuralp Journal of Mathematics (KJM), 9(2), 324–331.
dc.identifier.endpage331en_US
dc.identifier.issn2147-625X
dc.identifier.issue2en_US
dc.identifier.scopus2-s2.0-85161244863en_US
dc.identifier.scopusqualityN/Aen_US
dc.identifier.startpage324en_US
dc.identifier.urihttps://hdl.handle.net/11467/8844
dc.identifier.volume9en_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherKonuralp Journal of Mathematicsen_US
dc.relation.ispartofKonuralp Journal of Mathematicsen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectBayesian Regularization Neural Networken_US
dc.subjectCOVID-19en_US
dc.subjectDermatological Findingsen_US
dc.subjectLevenberg-–Marquardt Neural Networken_US
dc.subjectScaled Conjugate Gradient Neural Networken_US
dc.titleComparative analysis of neural networks in the diagnosis of emerging diseases based on COVID-19en_US
dc.typeArticleen_US

Dosyalar

Orijinal paket
Listeleniyor 1 - 1 / 1
Küçük Resim
İsim:
Comparative analysis of neural networks in the diagnosis of emerging diseases based on COVID-19.pdf
Boyut:
400.22 KB
Biçim:
Adobe Portable Document Format
İndir

Koleksiyon

Scopus İndeksli Yayınlar Koleksiyonu
İTBF, Matematik Bölümü Koleksiyonu