A Brief Perusal of Image-based Diagnosis for COVID-19 Using Image Processing Perspective

A coronavirus pandemic is brought on by a worldwide health crisis. In March 2020, the World Health Organisation declared COVID-19 to be a global pandemic. By visually analysing their CXRimages, (Machine Learning) techniques may be the main tool used in the identification of COVID-19 patients. This work proposes a novel approach to image processing (I.P.) that will classify C.X.R. images into two groups: non-COVID-19 and patient COVID-19. Recent Fractional Multichannel Exponent Moments (FrMEMs) of chest x-ray images were used to extract certain features. To expedite the computation process, parallel multi-core computing architecture is used. Following that, D.E. (Differential Evolution) is used by an updated Manta Ray Foraging Optimisation (MRFO) to select key components. The suggested approach has been applied to the analysis of two COVID-19 X-ray datasets. The proposed approach achieved precision values for 1st and 2nd datasets, respectively of 96.09% and 98.09%.