Paper Title
Two Approaches for Lung Covid-19 Infection Classification on CT Image

Abstract
In early 2020, an existential health catastrophe resulted in the worldwide spread of the Coronavirus 2019 (COVID-19). Traditional healthcare strategies for treating COVID-19 may be improved with the use of automated CT imaging identification of lung infections like COVID-19. COVID-19 infections can only be diagnosed by using CT imaging. CT images are obtained from two databases: CC-CCII and MosMedData, which are used for improvement purposes of lung COVID-19 abnormality classification on CT scans. The significant heterogeneity and low density between infected and normal tissues make it difficult to identify infection using CT scans. The earlier COVID-19 infection predicate is used to perform a range of diagnostic activities, which helps identify pathological (COVID-19 infections) as well as enhances CT diagnostic reporting accuracy. Finally, modified machine learning models (CNN and SVM) were used to classify CT images as COIV-19 infection or normal. Analysis of experimental and clinical data shows that the proposed methodologies for examining the variability of the internal geometric characteristics (classification) of the lung and COVID-19 infections in images are effective. The modified systems showed that the accuracy of SVM with combined LBP with HOG is 98% and modified CNN of 98%. Keywords - Image Processing, COVID-19 Infections, Classification, Feature Extraction, CT Image, Machine Learning.