Prediction of COVID-19 using procedures of transfer learning

Abstract

The potential impact of these improvements and future directions on advancing the field of COVID-19 prediction using transfer learning approaches in medical imaging is significant. By addressing the limitations and challenges of the current model, such as dataset imbalance and limited feature representation, these advancements could lead to more accurate and reliable predictions of COVID-19 from chest X-ray images. This, in turn, could aid healthcare professionals in early de-tection, diagnosis, and management of COVID-19 cases, ultimately contributing to better patient outcomes and public health efforts. Moreover, the development of robust and generalizable models for COVID-19 prediction could have broader implications beyond the current pandemic, serving as a foundation for future research in computer-aided diagnosis and disease prognosis using medical imaging data.