A Neural Network-Based Comparative Analysis for the Diagnosis of Emerging Different Diseases Based on COVID-19

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Kirişci M., Demir İ., Şimşek N.

10th International Eurasian Conference on Mathematical Sciences and Applications (IECMSA-2021), Sakarya, Türkiye, 25 - 27 Ağustos 2021, cilt.4, sa.3, ss.298-302, (Tam Metin Bildiri)

  • Yayın Türü: Bildiri / Tam Metin Bildiri
  • Cilt numarası: 4
  • Basıldığı Şehir: Sakarya
  • Basıldığı Ülke: Türkiye
  • Sayfa Sayıları: ss.298-302
  • Açık Arşiv Koleksiyonu: AVESİS Açık Erişim Koleksiyonu
  • İstanbul Üniversitesi-Cerrahpaşa Adresli: Evet

Özet

Dermatological diseases are frequently encountered in children and adults for various reasons. Many factors 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. In this investigation, the prediction capabilities of artificial neural networks were compared. 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 accuracy. For this reason, artificial neural networks can classify these diseases as accurately as human experts in an experimental setting.