3d8d13581898e639443-2ebe 20230923053251762 beie:beie director@blueeyesintelligence.org WEB-FORM International Journal of Innovative Technology and Exploring Engineering IJITEE 22783075 10.35940/ijitee https://www.ijitee.org/ 09 30 2023 12 10 Department of Data Science, University of Mutah, Karak, Jordan. Rania Alhalaseh https://orcid.org/0009-0000-1145-6001 Mohammad Abbadi https://orcid.org/0000-0003-1601-627X Department of Computer Science, University of Mutah, Karak, Jordan. Sura Kassasbeh https://orcid.org/0009-0006-2711-2872 Department of Computer Science, University of Mutah, Karak, Jordan. Since the spread of the COVID-19 pandemic, the number of patients has increased dramatically, making it difficult for medical staff, including doctors, to cover hospitals and monitor patients. Therefore, this work depends on Computerized Tomography (CT) scan images to diagnose COVID-19. CT scan images are used to diagnose and determine the severity of the disease. On the other hand, Deep Learning (DL) is widely used in medical research, making great progress in medical technologies. For the diagnosis process, the Convolutional Neural Network (CNN) algorithm is used as a type of DL algorithm. Hence, this work focuses on detecting COVID-19 from CT scan images and determining the severity of the illness. The proposed model is as follows: first, classifying CT scan images into infected or not infected using one of the CNN structures, Residual Neural Networks (ResNet50); second, applying a segmentation process for the infected images to identify lungs and pneumonia using the SegNet algorithm (a CNN architecture for semantic pixel-wise segmentation) so that the disease's severity can be determined; finally, applying linear regression to predict the disease's severity for any new image. The proposed approach reached an accuracy of 95.7% in the classification process and lung and pneumonia segmentation of 98.6% and 96.2%, respectively. Furthermore, a regression process reached an accuracy of 98.29%. 09 30 2023 1 14 CC BY-NC-ND 4.0 10.35940/BEIESP.CrossMarkPolicy www.ijitee.org true International Journal of Innovative Technology and Exploring Engineering (IJITEE) No, I did not receive. No conflicts of interest to the best of our knowledge. No, the article does not require ethical approval and consent to participate with evidence. Not relevant. The idea of the work was conceptualized by Mohammad Abbadi and Rania Alhalaseh. Methodology and validation were provided by Rania ALhalaseh. Software implementation and visualization were performed by Sura Kassasbeh. Writing---original-draft preparation was performed by Sura Kassasbeh, and writing---review and editing were performed by Rania Alhalaseh and Mohammad Abbadi. Project administration was performed by Mohammad Abbadi. 10.35940/ijitee.J9725.09121023 https://www.ijitee.org/portfolio-item/J972509121023/