88
Views
8
CrossRef citations to date
0
Altmetric
Research Article

Meta-analysis of predictions of COVID-19 disease based on CT-scan and X-ray images

, &
 

Abstract

World is facing a pandemic from last few months entitled as COVID-19 caused by novel CORONA VIRUS. The outbreak of this disease started in year ending of 2019 from Wuhan, China and has affected almost all countries in the world. Till now, there is no vaccine for this disease however researchers are doing their best to find vaccine for the particular. Medical Imaging techniques are playing vital role in helping doctors to treat patients by giving detailed information about internal body parts of humans. The outbreak of COVID-19 has encouraged researchers to develop medical imaging-based solutions so that medical team can diagnose patients infected with COVID-19 in less time and required treatment can be started on time. X-ray and CT scan are two important techniques in field of medical imaging. Main objective of presented study is to perform meta-analysis of the studies that proposed neural network-based classification/segmentation models for diagnosing COVID-19 disease on CT scan or X-rays. Meta-analysis has been performed on 35 studies on X-ray and 35 studies on CT-scan images for prediction of COVID-19. Analysis suggests that there is heterogeneity in publications to predict the COVID-19 disease on CT scans and X-rays using deep learning models. However, no publication biasness is found in both the studies.

Subject Classification:

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.