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Automated diagnosis of COVID stages using texture‐based Gabor features in variational mode decomposition from CT images by Rajneesh Kumar Patel; Manish Kashyap is a Computer Science article available to read on EtoBox.

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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; however, none are effective in detecting COVID at the preliminary phase. We propose a method based on twodimensional variational mode decomposition in this work. This proposed approach decomposes pre-processed CT scan pictures into sub-bands. The texturebased Gabor filter bank extracts the relevant features, and the student's t-value is used to recognize robust traits. After that, linear discriminative analysis (LDA) reduces the dimensionality of features and provides ranks for robust features. Only the first 14 LDA features are qualified for classification. Finally, the least squaresupport vector machine (SVM) (radial basis function) classifier distinguishes between COVID and no

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Author

Rajneesh Kumar Patel; Manish Kashyap

Publisher

Wiley

Published

2023

Language

EN

Field

Computer Science (Physical Sciences)