Diabetic　retinopathy　(DR)　is　one　of　the　major　causes　of　blindness.　It　is　of　great　significance　to　apply　deep-learning　techniques　for　DR　recognition.　However,　deep-learning　algorithms　often　depend　on　large　amounts　of　labeled　data,　which　is　expensive　and　time-consuming　to　obtain　in　the　medical　imaging　area.　In　addition,　the　DR　features　are　inconspicuous　and　spread　out　over　high-resolution　fundus　images.　Therefore,　it　is　a　big　challenge　to　learn　the　distribution　of　such　DR　features.　This　article　proposes　a　multichannel-based　generative　adversarial　network　(MGAN)　with　semisupervision　to　grade　DR.　The　multichannel　generative　model　is　developed　to　generate　a　series　of　subfundus　images　corresponding　to　the　scattering　DR　features.　By　minimizing　the　dependence　on　labeled　data,　the　proposed　semisupervised　MGAN　can　identify　the　inconspicuous　lesion　features　by　using　high-resolution　fundus　images　without　compression.　Experimental　results　on　the　public　Messidor　data　set　show　that　the　proposed　model　can　grade　DR　effectively.　IEEE