Retinal　diseases　such　as　age-related　macular　degeneration　and　diabetic　macular　edema　will　lead　to　irreversible　blindness　without　timely　diagnosis　and　treatment.　Optical　coherence　tomography　(OCT)　has　been　widely　utilized　to　detect　retinal　diseases　because　of　its　non-contact　and　non-invasive　imaging　peculiarities.　Due　to　the　lack　of　ophthalmic　medical　resources,　automatic　analyzing　and　diagnosing　retinal　OCT　images　is　necessary　with　computer-aided　diagnosis　algorithms.　In　this　study,　we　propose　a　lightweight　retinal　OCT　image　classification　model　integrating　convolutional　neural　network　(CNN)　and　Transformer　to　classify　various　retinal　diseases　with　few　parameters　of　the　model.　Local　lesion　features　extracted　by　CNN　can　be　encoded　with　the　whole　OCT　image　through　the　Transformer,　which　improves　the　classification　ability.　A　convolutional　block　attention　module　is　also　integrated　into　our　model　to　enhance　the　representational　power.　Compared　with　several　classical　models,　our　model　achieves　the　best　accuracy　of　0.9800　and　recall　of　0.9799　with　the　least　number　of　parameters　and　prediction　time　for　an　image　on　the　OCT-C8　dataset.　Moreover,　on　the　OCT2017　dataset,　our　model　outperforms　the　four　state-of-the-art　models　except　almost　equal　to　another,　achieving　an　average　accuracy,　precision,　recall,　specificity　and　F1-score　of　0.9985,　0.9970,　0.9970,　0.9990,　and　0.9970.　Simultaneously,　the　number　of　parameters　of　our　model　has　been　reduced　to　just　1.28　M,　and　the　average　prediction　time　for　an　image　is　only　2.5　ms.　©　2024　Elsevier　Ltd