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

Abstract

AbstractCOVID‐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 two‐dimensional variational mode decomposition in this work. This proposed approach decomposes pre‐processed CT scan pictures into sub‐bands. The texture‐based 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 square‐ support vector machine (SVM) (radial basis function) classifier distinguishes between COVID and non‐COVID CT lung images. The results of the trial showed that our model outperformed cutting‐edge methods for COVID classification. Using tenfold cross‐validation, this model achieved an improved classification accuracy of 93.96%, a specificity of 95.59%, and an F1 score of 93%. To validate our proposed methodology, we conducted different relative experiments with deep learning and traditional machine learning‐based models like random forest, K‐nearest neighbor, SVM, convolutional neural network, and recurrent neural network. The proposed model is ready to help radiologists identify diseases daily.