Prediction of Covid-19 using Self-Supervised Learning and CNN

Prashant Sharma Prashant Sharma; Sonal Sharma

doi:10.59890/ijsss.v1i1.51

Authors

Prashant Sharma Prashant Sharma Mandsaur University
Sonal Sharma Jain University

DOI:

https://doi.org/10.59890/ijsss.v1i1.51

Keywords:

COVID-19, VGG-16, CT- Scan, RT-PCR, Convolutional Neural Network (CNN)

Abstract

Corona Disease (COVID19) is an infectious disease that is rapidly producing respiratory contaminations and demanding medical attention on a global scale. It has had a profound effect on people lives, society, and the global economy. Due to limitations of RT-PCR (switch record polymerase chain reaction)-based methods for COVID19 diagnosis, various healthcare-based imaging approaches have lately been used. In this paper, we took a freely available image dataset with an extensive number of CT scans which were positive for COVID-19. Then utilizing a least amount of training X-Ray images, we build sample-efficient deep learning approaches that really an extensive testing demonstrated that the suggested Self-Trans method to achieves a lot of cutting-edge benchmarks. The proposed approach detects COVID-19 instances with a reliability rate of 98.7% and COVID-19 infection could be predicted with high precision by CT imaging

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