Agricultural and Biological Research

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.