ABSTRACT
Computed tomography is critical in diagnosing and assessing COVID-19 infection. Coronavirus (COVID-19) spread around the world in 2020, leaving the world facing an acute health crisis. The automatic deletion of lung infection on computed tomography scan (CT) images offers great potential for improving traditional healthcare strategies for treating COVID-19. However, the detection of lesions via CT imaging faces many challenges, including high contrast in infection characteristics and low contrast intensity between infection and normal tissues. Early diagnosis is an effective way to treat this condition. Then offered a deep learning pipeline consists of three different deep learning structures for generating and segmenting computed tomography of lungs and COVID-19 infection. In addition to this image pre-processing, image magnification and parameter correction based on the color model and model similarity were used to improve the diagnostic process (medium and strong infection areas). © 2022 American Institute of Physics Inc.. All rights reserved.