Facilitating COVID recognition from X-rays with computer vision models and transfer learning
Document Type
Article
Publication Date
1-1-2024
Journal / Book Title
Multimedia Tools and Applications
Abstract
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 few images from publicly available big data on chest X-rays, suitably adapting computer vision models with data augmentation. Secondly, we aim to find the best fit models to solve this problem, adjusting the number of samples for training and validation to obtain the minimum number of samples with maximum accuracy. Thirdly, our results indicate that combining chest radiography with transfer learning has the potential to improve the accuracy and timeliness of radiological interpretations of COVID in a cost-effective manner. 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.
DOI
10.1007/s11042-023-15744-9
Journal ISSN / Book ISBN
85160300879 (Scopus)
Montclair State University Digital Commons Citation
Varde, Aparna S.; Karthikeyan, Divydharshini; and Wang, Weitian, "Facilitating COVID recognition from X-rays with computer vision models and transfer learning" (2024). School of Computing Faculty Scholarship and Creative Works. 70.
https://digitalcommons.montclair.edu/computing-facpubs/70
Published Citation
Varde AS, Karthikeyan D, Wang W. Facilitating COVID recognition from X-rays with computer vision models and transfer learning. Multimed Tools Appl. 2023 May 26:1-32. doi: 10.1007/s11042-023-15744-9.
Comments
This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.