2023
DOI: 10.1002/ima.22865
|View full text |Cite
|
Sign up to set email alerts
|

Automated diagnosis of COVID stages using texture‐based Gabor features in variational mode decomposition from CT images

Abstract: COVID-19 is a deadly and fast-spreading disease that makes early death by affecting human organs, primarily the lungs. The detection of COVID in the early stages is crucial as it may help restrict the spread of the progress. The traditional and trending tools are manual, time-inefficient, and less accurate. Hence, an automated diagnosis of COVID is needed to detect COVID in the early stages. Recently, several methods for exploiting computed tomography (CT) scan pictures to detect COVID have been developed; how… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
1
1
1

Citation Types

0
0
0

Year Published

2023
2023
2024
2024

Publication Types

Select...
7

Relationship

0
7

Authors

Journals

citations
Cited by 8 publications
(3 citation statements)
references
References 60 publications
0
0
0
Order By: Relevance
“…Discrete cosine transform is used in the second step to choose high-variance features from each block and align them with the neural network for classifcation and a 99.7% accuracy was attained. Saba et al [13] proposed the region of interest (ROI) of posttraumatic stress disorder (PTSD) in the brain using resting-state functional magnetic resonance imaging (rs-fMRI), and machine learning algorithms were used to distinguish between PTSD [21]. Further, LS-SVM was used and achieved a classifcation accuracy of 95.48%, a specifcity of 95.37%, a sensitivity of 95.43%, and an F1 score of 95 [22].…”
Section: Related Workmentioning
confidence: 99%
“…Discrete cosine transform is used in the second step to choose high-variance features from each block and align them with the neural network for classifcation and a 99.7% accuracy was attained. Saba et al [13] proposed the region of interest (ROI) of posttraumatic stress disorder (PTSD) in the brain using resting-state functional magnetic resonance imaging (rs-fMRI), and machine learning algorithms were used to distinguish between PTSD [21]. Further, LS-SVM was used and achieved a classifcation accuracy of 95.48%, a specifcity of 95.37%, a sensitivity of 95.43%, and an F1 score of 95 [22].…”
Section: Related Workmentioning
confidence: 99%
“…The thermal, visible, and fused tongue images were converted into grayscale images to extract the statistical features. The Gray level co-occurrence matrix (GLCM) relies on a statistical technique employed to examine texture features, providing insights into the spatial pixel relationships [38][39][40][41] . We extracted the statistical parameters such as mean, contrast, standard deviation, correlation, energy, entropy, homogeneity, skewness, variance, and kurtosis from the thermal, visible, and fused tongue images using the GLCM algorithm.…”
Section: Statistical Feature Extractionmentioning
confidence: 99%
“…To achieve precise lung segmentation, it combines DL algorithms with cutting-edge image processing techniques. An automated diagnosis system for COVID stages is presented by Patel and Kashyap [22] using texturebased Gabor features in variational mode decomposition from CT scans. The authors use Gabor filters to extract discriminative texture features from the CT images after breaking them down into texture and structure components.…”
Section: Literature Reviewmentioning
confidence: 99%