A model for effective prediction of COVID-19 disease using d | 105523
An International Journal

Agricultural and Biological Research

ISSN - 0970-1907
RNI # 24/103/2012-R1


A model for effective prediction of COVID-19 disease using deep learning

Kanoori Jyothi*, Ramu Vankudoth, Gugloth Ganesh, J. Sanyasamma and S. Shiva Prasad

The coronavirus disease (COVID-19) pandemic represents a major global health challenge, requiring prompt and accurate prediction models for effective disease management. This review paper proposes a deep learning-based model to predict new coronavirus infections, utilizing clinical data and radiological imaging to improve accuracy and reliability. The model architecture includes a Convolutional Neural Network (CNN) for image analysis and a Recurrent Neural Network (RNN) for temporal data processing. The performance of the model is evaluated on various datasets and its effectiveness is compared with existing prediction methods. The results demonstrate the model's potential as a valuable tool in early diagnosis, resource allocation, and pandemic preparedness in healthcare systems.

Journal Hilights
  • Abstracting and indexing in renowned databases
  • Expert editorial team
  • High quality articles
  • High visibility
  • International readership
  • Language editing
  • Membership
  • Online manuscript submission and tracking system
  • Rapid peer review process
  • Reprints of published articles
Journal is Indexed in:
  • BIOSIS Previews and Zoological Record which are part of the life sciences in Web of Science (WOS)
  • Euro Pub
  • Google Scholar
  • MIAR
  • Publons
Journal Flyer
Google Scholar Citation Report
Citation image
Peer Review Process Check
Publon image