Texture analysis improves lung-tissue segmentation on high-resolution computed tomography in COVID-19

Texture-based feature extraction achieved 88.6% ROI-level accuracy and can serve as a supplementary tool during radiological interpretation of chest CT.

Authors

Mohamed Elfadil Mohamed Gar-elnabi
Mazin Abdalla Hassib
Amjad Rashed Alyahyawi
Mohamed S. Ali (ORCID: https://orcid.org/0000-0001-6718-3540)
Mohammed Salih (ORCID: https://orcid.org/0000-0002-5916-7257)
Mamdouh S. Al-enezi
Ahmed Babikir Abdalla Hasieb

Institutions

University of Ha'il (SA)
National University (SD)
Dammam Central Hospital (SA)

Publication Details

Journal: Frontiers in Radiology
Published: 2025-12-05
DOI: https://doi.org/10.3389/fradi.2025.1694478
Primary Topic: COVID-19 diagnosis using AI
Type: article
Field-Weighted Citation Impact: 0.00

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article

Texture analysis improves lung-tissue segmentation on high-resolution computed tomography in COVID-19

Mohamed Elfadil Mohamed Gar-elnabi, Mazin Abdalla Hassib, Amjad Rashed Alyahyawi, Mohamed S. Ali et al.

Dec 5, 2025

Frontiers in Radiology

COVID-19 diagnosis using AI

article

Texture analysis improves lung-tissue segmentation on high-resolution computed tomography in COVID-19

Mohamed Elfadil Mohamed Gar-elnabi, Mazin Abdalla Hassib, Amjad Rashed Alyahyawi, Mohamed S. Ali, Mohammed Salih, Mamdouh S. Al-enezi, Ahmed Babikir Abdalla Hasieb

December 5, 2025article en

Abstract

Texture-based feature extraction achieved 88.6% ROI-level accuracy and can serve as a supplementary tool during radiological interpretation of chest CT.

Frontiers in RadiologyVol. 5

University of Ha'il (SA), National University (SD), Dammam Central Hospital (SA)

Openalex Percentile: Top 47%

COVID-19 diagnosis using AI

DOI Open Access PDF OpenAlex

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Texture analysis improves lung-tissue segmentation on high-resolution computed tomography in COVID-19 — Mohamed Elfadil Mohamed Gar-elnabi, Mazin Abdalla Hassib, et al. · Frontiers in Radiology (2025) | TGRS Research Map | TGRS