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.