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JOIG 2024 Vol.12(1): 66-75
doi: 10.18178/joig.12.1.66-75

Normalized-UNet Segmentation for COVID-19 Utilizing an Encoder-Decoder Connection Layer Block

Mohammed Al-Mukhtar 1,*, Ammar Awni Abbas 2, Aws H. Hamad 3, and Mina H. Al-hashimi 4
1. Computer Center, University of Baghdad, Baghdad, Iraq
2. College of Mass Media Baghdad, University of Baghdad, Baghdad, Iraq
3. The Iraqi Ministry of Higher Education, Research and Development Department, Baghdad, Iraq
4. Department of Computer Engineering, Al-Mansour University College, Baghdad, Iraq
Email: mohammed.abdul@cc.uobaghdad.edu.iq (M.A.-M.); ammarawne@yahoo.com (A.A.A.); aws.hamed@rdd.edu.iq (A.H.H.); minaalhashimi082@gmail.com (M.H.A.)
*Corresponding author

Manuscript received August 10, 2023; revised September 19, 2023; accepted October 23, 2023; published March 8, 2024.

Abstract—The COVID-19 pandemic has had a huge influence on human lives all around the world. The virus spread quickly and impacted millions of individuals, resulting in a large number of hospitalizations and fatalities. The pandemic has also impacted economics, education, and social connections, among other aspects of life. Coronavirus-generated Computed Tomography (CT) scans have Regions of Interest (ROIs). The use of a modified U-Net model structure to categorize the region of interest at the pixel level is a promising strategy that may increase the accuracy of detecting COVID-19-associated anomalies in CT images. The suggested method seeks to detect and isolate ROIs in CT scans that show the existence of ground-glass opacity, which is frequent in COVID-19 patients. This can assist healthcare practitioners in identifying and monitoring illness development, as well as making treatment decisions. Scale U-Net is a strong U-Net design modification that can increase the performance of semantic segmentation tasks. Our model, Normalized-UNet, uses batch normalization after each convolutional layer to decrease the internal covariate shift, which dramatically improves the network's learning efficiency.

Keywords—normalized-UNet, U-Net, COVID-19, scale U-Net

Cite: Mohammed Al-Mukhtar, Ammar Awni Abbas, Aws H. Hamad, and Mina H. Al-hashimi, "Normalized-UNet Segmentation for COVID-19 Utilizing an Encoder-Decoder Connection Layer Block," Journal of Image and Graphics, Vol. 12, No. 1, pp. 66-75, 2024.

Copyright © 2024 by the authors. This is an open access article distributed under the Creative Commons Attribution License (CC BY-NC-ND 4.0), which permits use, distribution and reproduction in any medium, provided that the article is properly cited, the use is non-commercial and no modifications or adaptations are made.