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.　©　2022　Elsevier　Ltd