COVID-19 is a highly contagious and virulent disease caused by the severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2). COVID-19 disease induces lung changes observed in lung computerized tomography (CT) and the percentage of those diseased areas on the CT correlates with the severity of the disease. Therefore, segmentation of CT images to delineate the diseased or lesioned areas is a logical first step to quantify disease severity, which will help physicians predict disease prognosis and guide early treatments to deliver more positive patient outcomes. It is crucial to develop an automated analysis of CT images to save their time and efforts. This dissertation proposes CoviNet, a deep three-dimensional convolutional neural network (3D-CNN) to diagnose COVID-19 in CT images. It also proposes CoviNet Enhanced, a hybrid approach with 3D-CNN and support vector machines. It also proposes CoviSegNet and CoviSegNet Enhanced, which are enhanced U-Net models to segment ground-glass opacities and consolidations observed in computerized tomography (CT) images of COVID-19 patients. We trained and tested the proposed approaches using several public datasets of CT images. The experimental results show the proposed methods are highly effective for COVID-19 detection and segmentation and exhibit better accuracy, precision, sensitivity, specificity, F-1 score, Matthew's correlation coefficient (MCC), dice score, and Jaccard index in comparison with recently published studies.
