Automatic Covid Detection using CT Images

Abstract

Abstract: COVID-19, the disease caused by the novel corona virus, can cause lung complications such as pneumonia and, in the most severe cases, acute respiratory distress syndrome, or ARDS. Another possible complication of COVID-19 is sepsis, which can cause long-term damage to the lungs and other organs. COVID-19 virus is primarily transmitted through droplets produced when an infected person coughs, sneezes, or exhales. These droplets are too heavy to float in the air and quickly fall to the ground or other surfaces. As everyone is aware, the corona virus disease 2019 (COVID-19) spread throughout the world in early 2020, causing the world to face an existential health crisis. Thus, automating the detection of lung infections from computed tomography (CT) images has the potential to supplement the traditional healthcare strategy for combating COVID-19. However, segmenting infected regions from CT slices is difficult due to high variation in infection characteristics and low-intensity contrast between infections and normal tissues. Furthermore, collecting a large amount of data in a short period of time is impractical. Our proposed solution will analyse a CT image of the lung and detect the infected portion of the lung, as well as the percentage of the affected portion. The system will identify the infection severity and will help patients to take essential measures. Keywords: Preprocessing, Segmentation, Feature Ex-traction, Classification, CT Images, Image Preprocessing.