Covid-19 Infection Segmentation Using Deep Learning Techniques

ABSTRACT

The rapid spread of the disease after COVID-19's emergence in 2019 has presented enormous problems to medical institutions. The diagnosis process will go more rapidly if the infected region in the COVID-19 CT image can be automatically segmented, which will aid clinicians in promptly identifying the patient's illness. Automated lung infection identification using computed tomography scans is a more general approach. However, segmenting sick areas from CT slices is quite difficult. In this work, a diagnosis system based on deep learning methods is being created to identify and quantify COVID-19 infection and screen for pneumonia using CT imaging. Here, Unet++ approaches, U-net architecture based on CNN encoder and CNN decoder, and Attention Unet segmentation techniques are used. These methods are applied for quick and accurate picture segmentation to produce segmentation models for lung and infection. Fourfold cross-validation has been used as a re-sampling method to improve skill estimate on unseen data. To enable volume ratio calculating and determine infection rate, the lung and infection volumes have been reconfigured. 20 CT scan cases were used in this study, and the data were split into two, training dataset 70% and a validation dataset 30%. In this study with three architectures it shows that basic Unet performs well compared to other two architectures. © 2022 IEEE