Deep Learning Approach for Advanced COVID-19 Analysis
Rania Alhalaseh1, Mohammad Abbadi2, Sura Kassasbeh3
1Rania Alhalaseh, Department of Data Science, University of Mutah, Karak, Jordan.
2Mohammad Abbadi, Department of Computer Science, University of Mutah, Karak, Jordan.
3Sura Kassasbeh, Department of Computer Science, University of Mutah, Karak, Jordan.
Manuscript received on 28 August 2023 | Revised Manuscript received on 05 September 2023 | Manuscript Accepted on 15 September 2023 | Manuscript published on 30 September 2023 | PP: 1-14 | Volume-12 Issue-10, September 2023 | Retrieval Number: 100.1/ijitee.J972509121023 | DOI: 10.35940/ijitee.J9725.09121023
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: 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 processfor 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%.
Keywords: Convolutional Neural Network (CNN), Deep Learning, Severity, Segnet, ResNet50, CT scan, VGG16.
Scope of the Article: Deep learning