Diabetic　retinopathy　(DR)　is　a　visual　obstacle　caused　by　diabetic　disease,　which　forms　because　of　long-standing　diabetes　mellitus,　which　damages　the　retinal　blood　vessels.　This　disease　is　considered　one　of　the　principal　causes　of　sightlessness　and　accounts　for　more　than　158　million　cases　all　over　the　world.　Since　early　detection　and　classification　could　diminish　the　visual　impairment,　it　is　significant　to　develop　an　automated　DR　diagnosis　method.　Although　deep　learning　models　provide　automatic　feature　extraction　and　classification,　training　such　models　from　scratch　requires　a　larger　annotated　dataset.　The　availability　of　annotated　training　datasets　is　considered　a　core　issue　for　implementing　deep　learning　in　the　classification　of　medical　images.　The　models　based　on　transfer　learning　are　widely　adopted　by　the　researchers　to　overcome　annotated　data　insufficiency　problems　and　computational　overhead.　In　the　proposed　study,　features　are　extracted　from　fundus　images　using　the　pre-trained　network　VGGNet　and　combined　with　the　concept　of　transfer　learning　to　improve　classification　performance.　To　deal　with　data　insufficiency　and　unbalancing　problems,　we　employed　various　data　augmentation　operations　differently　on　each　grade　of　DR.　The　results　of　the　experiment　indicate　that　the　proposed　framework　(which　is　evaluated　on　the　benchmark　dataset)　outperformed　advanced　methods　in　terms　of　accurateness.　Our　technique,　in　combination　with　handcrafted　features,　could　be　used　to　improve　classification　accuracy.