COVID-19 (SARS-COV2) visual digital data fusion using hybrid technique

Abstract

A COVID 19 outbreak caused by the new SARSCoV2 virus was declared by the World Health Organization (WHO) in March 2020. Since then, other studies have used Chest Xray or CT scans to identify this infection. Often, one aspect of the study is that these XRAY or CT scans of Covid patients have to be enhanced. The purpose of picture fusion is to merge complimentary, multi-sensor and/or multi-view images. Our major purpose of our work is to assist doctors speed up treatments in order to give their patients the most effective remedies as soon as possible. This study employs two multi-view data sets, which are merged using hybrid methodology and divided into two phases, as input images for our system. In first stage we use two fusion rules of Dual tree Complex Wavelet Transform (DT-CWT) and Discrete Cosine Transform (DCT) separately on both the images. In second stage we use fusion rule based on Singular Value Decomposition (SVD) on those fused images acquired from first stage. The performance of fused image is carried out by standard deviation (SD), root mean square (RMSE), peak signal to noise ratio (PSNR), percentage fit error (PEF), mean absolute error (MAE), mutual information (MI), quality index (QI) and measure of structural similarity (SSIM). © 2021 Author(s).