Comparative analysis of neural networks in the diagnosis of emerging diseases based on COVID-19
Yükleniyor...
Tarih
2021
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Konuralp Journal of Mathematics
Erişim Hakkı
info:eu-repo/semantics/openAccess
Özet
Dermatological 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.
Açıklama
Anahtar Kelimeler
Bayesian Regularization Neural Network, COVID-19, Dermatological Findings, Levenberg-–Marquardt Neural Network, Scaled Conjugate Gradient Neural Network
Kaynak
Konuralp Journal of Mathematics
WoS Q Değeri
Scopus Q Değeri
N/A
Cilt
9
Sayı
2
Künye
Kirişci, 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.
