In order to identify diabetic retinopathy (DR) in retinal fundus images automatically, to reduce the workload of ophthalmologists, and to develop an assistant tool in detecting and diagnosing retinal diseases, automated detection of DR images which uses deep transfer learning approach based on the Inception-v3 model is proposed. In the Inception-v3 model trained by ImageNet datasets, the parameters of the previous layers were fixed while the last fully-connected layer of the model was retrained by fine-tuning on the dataset collected by ourselves. Experimental results manifested the performance of the proposed approach providing better predictions and highly reliable detection without specifying lesion-based features, and it could help make automated screening for early DR based on retinal fundus images in addition to assisting ophthalmologists in making a referral decision.