Automated screening of COVID-19 using two-dimensional variational mode decomposition and locally linear embedding. (September 2022) - Ma, Liyuan; Xu, Xipeng; Cui, Changcai; Lu, Jingyi; Hua, Qifeng; Sun, Hao
Abstract: In order to aid imaging physicians to effectively screen chest radiography medical images for presence of Coronavirus Disease 2019 (COVID-19), a novel computer aided diagnosis technology for automatic processing of COVID-19 images is proposed based on two-dimensional variational mode decomposition (2D-VMD) and locally linear embedding (LLE). 2D-VMD algorithm is used to decompose normal and COVID-19 images, and then feature extraction of intrinsic mode functions (IMFs) using Gabor filter. To better extract low-dimensional parameters which are useful for COVID-19 diagnosis, the performance of two dimensionality reduction techniques of principal component analysis (PCA) and LLE are compared, and the LLE is shown to offer satisfactory effect of dimension reduction. Thereafter, the particle swarm optimization-support vector machine (PSO-SVM) algorithm is used to classify. The simulation results show that the proposed technology has achieved accuracy of 99.33%, precision of 100%, recall of 98.63% and F-Measure of 99.31%. Hence, the developed diagnosis technology can be used as an important auxiliary tool to assist diagnosis of imaging physicians. Highlights: Gabor filter is used to extract features of decomposed IMFs by 2D-VMD algorithm. LLE algorithm is employed to reduce the data dimensions of redundant feature parameters. A novel automated method is successfully applied to screen normal and COVID-19 images.