COVID-19　has　caused　havoc　throughout　the　world　in　the　last　two　years　by　infecting　over　455　million　people.　Development　of　automatic　diagnosis　software　tools　for　rapid　screening　of　COVID-19　via　clinical　imaging　such　as　X-ray　is　vital　to　combat　this　pandemic.　An　optimized　deep　learning　model　is　designed　in　this　paper　to　perform　automatic　diagnosis　on　the　chest　X-ray　(CXR)　images　of　patients　and　classify　them　into　normal,　pneumonia　and　COVID-19　cases.　A　convolutional　neural　network　(CNN)　is　employed　in　optimized　deep　learning　model　given　its　excellent　performances　in　feature　extraction　and　classification.　A　particle　swarm　optimization　with　multiple　chaotic　initialization　scheme　(PSOMCIS)　is　also　designed　to　fine　tune　the　hyperparameters　of　CNN,　ensuring　the　proper　training　of　network.　The　proposed　deep　learning　model,　namely　PSOMCIS-CNN,　is　evaluated　using　a　public　database　consists　of　the　CXR　images　with　normal,　pneumonia　and　COVID-19　cases.　The　proposed　PSOMCIS-CNN　is　revealed　to　have　promising　performances　for　automatic　diagnosis　of　COVID-19　cases　by　producing　the　accuracy,　sensitivity,　specificity,　precision　and　F1　score　values　of　97.78%,　97.77%,　98.8%,　97.77%　and　97.77%,　respectively.　©　2023,　The　Author(s),　under　exclusive　license　to　Springer　Nature　Singapore　Pte　Ltd.