Diabetic　retinopathy　(DR)　and　diabetic　macular　edema　(DME)　are　the　major　causes　of　permanent　blindness　in　the　working-age　population.　Deep　learning　methods　have　been　proposed　to　automatically　grade　DR　and　DME　for　ophthalmologists＇　design　of　tailored　treatments　for　patients.　However,　these　methods　are　computationally　intensive　with　a　large　number　of　parameters　and　affect　the　optimization　of　hyperparameters,　making　them　challenging　to　deploy　to　mobile　or　embedded　devices　with　limited　computer　resources.　In　this　paper,　we　developed　a　transfer　learning-based　lightweight　convolutional　neural　network　to　jointly　classify　the　severity　of　DR　and　DME.　Using　fivefold　cross-validation,　our　model　achieved　an　average　accuracy,　precision,　recall,　specificity,　and　F1-score　of　0.9666,　0.9700,　0.9685,　0.9932,　and　0.9663,　respectively,　better　than　MobileNet　V2,　while　the　number　of　parameters　and　the　recognition　speed　were　dramatically　less　than　those　of　MobileNet　V2　and　ResNet50.　These　results　show　that　our　model　is　hopeful　in　diagnosing　retinopathy　in　clinical　trials,　even　when　configured　for　mobile　and　embedded　devices.