Enabling CT-Scans for covid detection using transfer learning-based neural networks

Abstract

Today, we are coping with the pandemic, and the novel virus is covertly evolving day by day. Therefore, a precautionary system to deal with the issue is required as early as possible. The last few years were very challenging for doctors, vaccine makers, hospitals, and medical authorities to deal with the massive crowd to provide results for all patients and newcomers in the past months. Thus, these issues should be handled with a robust system that can accord with many people and deliver the results in a fraction of time without visiting public places and help reduce crowd gathering. So, to deal with these issues, we developed an AI model using transfer learning that can aid doctors and other people to get to know whether they were suffering from covid or not. In this paper, we have used VGG-19 (CNN-based) model with open-sourced COVID-CT (CTSI) dataset. The dataset consists of 349 images of COVID-19 of 216 patients and 463 images of NON-COVID-19. We have achieved an accuracy of 95%, precision of 96%, recall of 94%, and F1-Score of 96% from the experiments.

Communicated by Ramaswamy H. Sarma

Keywords:

• Visual Geometry Group (VGG-19)
• COVID-19
• transfer learning
• artificial intelligence
• computed tomography scan images (CTSI)

Disclosure statement

No potential conflict of interest was reported by the authors.

Funding

The author(s) reported there is no funding associated with the work featured in this article.

Notes

1 https://www.kaggle.com/plameneduardo/sarscov2-ctscan-dataset

2 https://medpix.nlm.nih.gov/home

3 https://luna16.grand-challenge.org/

4 https://radiopaedia.org/articles/covid-19-3

5 https://www.ncbi.nlm.nih.gov/pmc/