Comparative Analysis of Neural Networks in the Diagnosis of Emerging Diseases based on COVID-19
No Thumbnail Available
Date
2021
Journal Title
Journal ISSN
Volume Title
Publisher
Prof. Dr. Mehmet Zeki SARIKAYA
Access Rights
info:eu-repo/semantics/closedAccess
Abstract
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. © 2021, Prof. Dr. Mehmet Zeki SARIKAYA. All rights reserved.
Description
Keywords
Bayesian Regularization Neural Network, COVID-19, dermatological findings, Levenberg-–Marquardt Neural Network, Scaled Conjugate Gradient Neural Network
Journal or Series
Konuralp Journal of Mathematics
WoS Q Value
Scopus Q Value
N/A
Volume
9
Issue
2
