Detection of COVID-19 Disease in Chest X-Ray Images with capsul networks: application with cloud computing

AKSOY B., Salman O. K. M.

Journal of Experimental and Theoretical Artificial Intelligence, cilt.33, sa.3, ss.527-541, 2021 (SCI-Expanded, Scopus) identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 33 Sayı: 3
  • Basım Tarihi: 2021
  • Doi Numarası: 10.1080/0952813x.2021.1908431
  • Dergi Adı: Journal of Experimental and Theoretical Artificial Intelligence
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, Applied Science & Technology Source, Business Source Elite, Business Source Premier, Compendex, Computer & Applied Sciences, INSPEC, Psycinfo, zbMATH
  • Sayfa Sayıları: ss.527-541
  • Anahtar Kelimeler: capsul Networks, cloud Computing, COVID-19, Deep Learning, medical Image Analysis
  • Isparta Uygulamalı Bilimler Üniversitesi Adresli: Evet

Özet

Today, health is the most important value of human life pandemics at different time intervals in the world history. Finally, the COVID-19 outbreak that occurred in Wuhan, China in December 2019, spread to the whole world in a really short time and caused a pandemic. In order to prevent this pandemic, early detection of the COVID-19 is very important. In this study, chest x-ray images of 1019 patients with open-source dataset were taken from four different sources. The images were analysed using Capsule Networks (CapsNet) model, which is one of the deep learning methods, whose popularity has increased in recent years. With the designed CapsNet model, individuals with COVID-19 disease were tried to be identified. The designed CapsNet model can detect COVID-19 disease with an accuracy rate of 98.02%. The obtained model cloud computing application was developed in order to use the work performed faster and easier.