Paper Title
Preprocessing of CT Imagery for Detection of Infection of Covid and Tuberculosis

Abstract
Lung diseases causes the death of many people every year. CT Scan are used for the diagnosis of the disease. However, even for a trained person, it is a challenge to examine the CT Images. It is necessary to improve the diagnosis accuracy. In this paper, the detecting of lung infection done using CT Images by taking the help of computer vision, AI and ML technologies. The data preprocessing system architecture is build to identify the percentage or amount of the region is infected of lung in image. That region of lung can be left lung lobe and right lung lobe and can be further divided as upper lobe, lower lobe. These lobes can be less Infected, severely Infected or moderately infected. The infection in lung is less Infected Image, severely Infected Image or moderately Infected Image is totally depended on the amount or percentage of region in lung image is Infected. So, to do this detection of infected region in an image, the Image pre-processing techniques and Connected Component Labeling Algorithm is used in this project. This paper gives exhaustive study on image pre- processing techniques for detecting the severity of Infection into 3 categories as less Infected, Moderately Infected and severely Infected. Keywords - COVID-19 Pneumonia, CT Images, Image preprocessing Techniques.