The　global　impact　of　COVID-19,　which　has　affected　over　700 million　individuals,　necessitates　the　development　of　automated　diagnostic　tools　for　rapid　screening　using　clinical　imaging,　such　as　X-rays.　Deep　learning　has　shown　remarkable　capabilities　in　feature　extraction　and　classification,　making　it　a　promising　technique　for　automatic　diagnosis　of　COVID-19　cases　through　analysis　of　chest　X-ray　(CXR)　images.　However,　achieving　optimal　classification　performance　with　deep　learning　models　relies　heavily　on　properly　setting　the　hyperparameters　during　the　transfer　learning　process,　presenting　a　nontrivial　challenge.　This　paper　introduces　sperm　swarm　optimization　(SSO),　an　emerging　metaheuristic　search　algorithm,　for　fine-tuning　four　key　hyperparameters　of　convolutional　neural　networks　(CNNs)　to　ensure　effective　training　of　the　network.　The　proposed　model,　SSOCNN,　is　evaluated　using　a　publicly　available　database　comprising　CXR　images　with　normal,　pneumonia,　and　COVID-19　cases.　Our　results　demonstrate　the　promising　performance　of　SSOCNN　in　automatic　diagnosis　of　COVID-19　cases,　achieving　accuracy,　sensitivity,　specificity,　precision,　and　F1　score　values　of　96.54%,　97.41%,　98.52%,　97.05%,　and　97.23%,　respectively.　©　The　Author(s),　under　exclusive　license　to　Springer　Nature　Singapore　Pte　Ltd.　2024.