The impact of super resolution on detecting COVID-19 from CT scans using VGG-16 based learning

Aburaed, N. and Panthakkan, A. and Al-Saad, M. and Al Mansoori, S. and Ahmad, Hussain Al (2021) The impact of super resolution on detecting COVID-19 from CT scans using VGG-16 based learning. Journal of Physics: Conference Series, 1828 (1). 012009. ISSN 1742-6588 (https://doi.org/10.1088/1742-6596/1828/1/012009)

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Abstract

With the recent outbreak of the novel Coronavirus (COVID-19), the importance of early and accurate diagnosis arises, as it directly affects mortality rates. Computed Tomography (CT) scans of the patients’ lungs is one of the diagnosis methods utilized in some countries, such as China. Manual inspection of CT scans can be a lengthy process, and may lead to inaccurate diagnosis. In this paper, a Deep Learning strategy based on VGG-16 is utilized with Transfer Learning for the purpose of binary classification of CT scans; Covid and NonCovid. Additionally, it is hypothesized in this study that Single Image Super Resolution (SISR) can boost the accuracy of the networks’ performance. This hypothesis is tested by following the training strategy with the original dataset as well as the same dataset scaled by a factor of ×2. Experimental results show that SISR has a positive effect on the overall training performance.

ORCID iDs

Aburaed, N. ORCID logoORCID: https://orcid.org/0000-0002-5906-0249, Panthakkan, A., Al-Saad, M., Al Mansoori, S. and Ahmad, Hussain Al;
CORE (COnnecting REpositories)