The novel 2019 coronavirus disease (COVID-19) has infected over 141 million people worldwide since April 20, 2021. More than 200 countries around the world have been affected by the coronavirus pandemic. Screening for COVID-19, we use fast and inexpensive images from computed tomography (CT) scans. In this paper, ResNet-50, VGG-16, convolutional neural network (CNN), convolutional auto-encoder neural network (CAENN), and machine learning (ML) methods are proposed for classifying Chest CT Images of COVID-19. The dataset consists of 1252 CT scans that are positive and 1230 CT scans that are negative for COVID-19 virus. The proposed models have priority over the other models that there is no need of pre-trained networks and data augmentation for them. The classification accuracies of ResNet-50, VGG-16, CNN, and CAENN were obtained 92.24%, 94.07%, 93.84%, and 93.04% respectively. Among ML classifiers, the nearest neighbor (NN) had the highest performance with an accuracy of 94%.
COVID-19 data analysis and prediction from patient data repository collected from hospitals and health organizations. Users' credentials and personal information are at risk; it could be an unrecoverable issue worldwide. A Homomorphic identification of possible breaches could be more appropriate for minimizing the risk factors in preventing personal data. Individual user privacy preservation is a must-needed research focus in various fields. Health data generated and collected information from multiple scenarios increasing the complexity involved in maintaining secret patient information. A homomorphic-based systematic approach with a deep learning process could reduce depicts and illegal functionality of unknown organizations trying to have relation to the environment and physical and social relations. This article addresses the homomorphic standard system functionality, which refers to all the functional aspects of deep learning system requirements in COVID-19 health management. Moreover, this paper spotlights the metric privacy incorporation for improving the Deep Learning System (DPLS) approaches for solving the healthcare system's complex issues. It is absorbed from the result analysis Homomorphic-based privacy observation metric gradually improves the effectiveness of the deep learning process in COVID-19-health care management.