Kirişci, MuratDemir, IbrahimŞimşek, Necip2024-10-122024-10-1220212147-625Xhttps://hdl.handle.net/11467/8844Dermatological 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.eninfo:eu-repo/semantics/closedAccessBayesian Regularization Neural NetworkCOVID-19dermatological findingsLevenberg-–Marquardt Neural NetworkScaled Conjugate Gradient Neural NetworkArticle92324331N/A2-s2.0-85161244863