This　research　offers　a　new　prediction　structure　coupling　improved　grey　wolf　optimization　(IGWO)　and　convolutional　neural　network　(CNN),　called　IGWO-CNN,　to　diagnose　diabetic　retinopathy.　Grey　wolf　optimizer　(GWO)　is　achieving　success　among　other　swarm　intelligence　procedures　due　to　its　broad　tuning　features,　scalability,　simplicity,　ease　of　use　and,　most　importantly,　its　ability　to　ensure　convergence　speed　by　providing　suitable　exploration　and　exploitation　throughout　a　search.　The　suggested　methodology　used　a　genetic　algorithm　(GA)　to　build　diversified　initial　positions.　GWO　was　subsequently　applied　to　adjust　existing　population　positions　in　the　discrete　search　procedure,　getting　the　optimal　feature　subset　for　a　higher　CNN-based　classification　challenge.　The　presented　technique　contrasts　with　GA,　GWO　and　numerous　existing　state-of-the-art　diabetic　retinopathy　classification　approaches.　The　suggested　strategy　outperforms　all　other　methods　by　increasing　classification　accuracy　to　98.33%,　indicating　its　efficacy　in　detecting　the　DR.　The　simulation　outcomes　have　shown　that　the　proposed　approach　outperforms　the　other　two　competing　methods.　©　2022,　The　Author(s),　under　exclusive　license　to　Springer　Nature　Singapore　Pte　Ltd.