Detecting COVID-19 Pneumonia over Fuzzy Image Enhancement on Computed Tomography Images.
Comput Math Methods Med
; 2022: 1043299, 2022.
Article
in English
| MEDLINE | ID: covidwho-1629752
ABSTRACT
COVID-19 is the worst pandemic that has hit the globe in recent history, causing an increase in deaths. As a result of this pandemic, a number of research interests emerged in several fields such as medicine, health informatics, medical imaging, artificial intelligence and social sciences. Lung infection or pneumonia is the regular complication of COVID-19, and Reverse Transcription Polymerase Chain Reaction (RT-PCR) and computed tomography (CT) have played important roles to diagnose the disease. This research proposes an image enhancement method employing fuzzy expected value to improve the quality of the image for the detection of COVID-19 pneumonia. The principal objective of this research is to detect COVID-19 in patients using CT scan images collected from different sources, which include patients suffering from pneumonia and healthy people. The method is based on fuzzy histogram equalization and is organized with the improvement of the image contrast using fuzzy normalized histogram of the image. The effectiveness of the algorithm has been justified over several experiments on different features of CT images of lung for COVID-19 patients, like Ground-Glass Opacity (GGO), crazy paving, and consolidation. Experimental investigations indicate that among the 254 patients, 81.89% had features on both lungs; 9.5% on the left lung; and 10.24% on the right lung. The predominantly affected lobe was the right lower lobe (79.53%).
Full text:
Available
Collection:
International databases
Database:
MEDLINE
Main subject:
Algorithms
/
Radiographic Image Enhancement
/
SARS-CoV-2
/
COVID-19
/
Lung
Type of study:
Observational study
/
Prognostic study
Limits:
Humans
Language:
English
Journal:
Comput Math Methods Med
Journal subject:
Medical Informatics
Year:
2022
Document Type:
Article
Affiliation country:
2022
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