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; 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 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.

Brain Tumor Detection and Classification Using IFF-FLICM Segmentation and Optimized ELM Model

“…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].…”

Tongue image fusion and analysis of thermal and visible images in diabetes mellitus using machine learning techniques

“…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.…”

Automatic detection of infected areas of CT images in COVID-19 patients using Inf-Seg-Net

“…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.…”