Detection of COVID-19 using wavelet transform

Abstract

Since December 2019, the world has been struggling Against the discovered virus called Covid-19, which Its symptoms are similar to pneumonia. Being highly contagious, it is It spread all over the world, hence the World Health Organization By declaring this disease as a global pandemic. some Patients infected with this virus suffer from severe symptoms And deadly. Hence the importance of early detection of Coronavirus (COVID-19). COVID-19 is a disease that affects the respiratory system of the human body, and detecting this disease is complex and one of the main challenges. This work proposed a technique to detect COVID-19 by integrating multifocal images based on wavelet transduction. So, to achieve the detection of COVID-19, Magnet resonant imagery (MRI) and computation tomography (CT) have been used. The multifocal image was included to support the diagnosis made by the clinicians. The seven wave-based algorithms bior2.2, coif2, db2, dmey, rbio2.2, sym4, and haar, respectively, were used to achieve a range of results. This approach effectively combines the data obtained from CT and MRI scans to produce a merged image that improves disease diagnosis efficiency by using MATLAB to determine the efficiency of the algorithm. The signal-to-noise ratio (PSNR) and the entropy factor are used to measure the image fusion efficiency. The statistical analysis of the final images demonstrated the superiority of the image attributes over both the CT image and the MRI.