Facilitating COVID recognition from X-rays with computer vision models and transfer learning.

Multimedia data plays an important role in medicine and healthcare since EHR (Electronic Health Records) entail complex images and videos for analyzing patient data. In this article, we hypothesize that transfer learning with computer vision can be adequately harnessed on such data, more specifically chest X-rays, to learn from a few images for assisting accurate, efficient recognition of COVID. While researchers have analyzed medical data (including COVID data) using computer vision models, the main contributions of our study entail the following. Firstly, we conduct transfer learning using a from publicly available big data on chest X-rays, suitably adapting computer vision models with . Secondly, we aim to find the to solve this problem, adjusting the number of samples for training and validation to obtain the of samples with Thirdly, our results indicate that combining chest radiography with transfer learning has the potential to improve the and of radiological interpretations of COVID in a . Finally, we outline applications of this work during COVID and its recovery phases with future issues for research and development. This research exemplifies the use of multimedia technology and machine learning in healthcare.