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Updated: Nov 17, 2025
Lung CT Segmentation to Identify Consolidations and Ground Glass Areas for Quantitative Assesment of SARS-CoV Pneumonia
Published on: December 19, 2020
Development of a novel computational method using computed tomography images for the early detection and severity
M A Abbas1,2, M S Alqahtani3, A J Alkulib3,4
1Electrical Engineering Department, College of Engineering, King Khalid University, Abha, Saudi Arabia.
Background:
Recent occurrence of the 2019 coronavirus disease (COVID-19) outbreak, caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has highlighted the need for fast, accurate, and simple strategies to identify cases on a large scale.
Objective:
This study aims to develop and test an accurate detection and severity classification methodology that may help medical professionals and non-radiologists recognize the behavior and propagation mechanisms of the virus by viewing computed tomography (CT) images of the lungs with implicit materials.
Methods:
In this study, the process of detecting the virus began with the deployment of a virtual material inside CT images of the lungs of 128 patients. Virtual material is a hypothetical material that can penetrate the healthy regions in the image by performing sequential numerical measurements to interpret images with high data accuracy. The proposed method also provides a segmented image of only the healthy parts of the lung.
Results:
The resulting segmented images, which represent healthy parts of the lung, are classified into six levels of severity. These levels are classified according to physical symptoms. The results of the proposed methodology are compared with those of the radiologists' reports. This comparison revealed that the gold-standard reports correlated with the results of the proposed methodology with a high accuracy rate of 93%.
Conclusion:
The study results indicate the possibility of relying on the proposed methodology for discovering the effects of the SARS-CoV-2 virus in the lungs through CT imaging analysis with limited dependency on radiologists.
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