###### Abstract  
Diabetic retinopathy (DR) is a dangerous eye condition that affects diabetic patients. Without early detection, it can affect the retina and may eventually cause permanent blindness. The early diagnosis of DR is crucial for its treatment. However, the diagnosis of DR is a very difficult process that requires an experienced ophthalmologist. A breakthrough in the field of artificial intelligence called deep learning can help in giving the ophthalmologist a second opinion regarding the classification of the DR by using an autonomous classifier. To accurately train a deep learning model to classify DR, an enormous number of images is required, and this is an important limitation in the DR domain. Transfer learning is a technique that can help in overcoming the scarcity of images. The main idea that is exploited by transfer learning is that a deep learning architecture, previously trained on non-medical images, can be fine-tuned to suit the DR dataset. This paper reviews research papers that focus on DR classification by using transfer learning to present the best existing methods to address this problem. This review can help future researchers to find out existing transfer learning methods to address the DR classification task and to show their differences in terms of performance. Diabetic retinopathy; deep learning; convolutional neural networks; transfer learning +
Footnote †: journal: Medical  
images to detect DR [9] because diabetic patients must be assessed by an ophthalmologist at least once a year to detect the early signs of DR. Therefore, a reliable detection technology is needed to assist health care personnel in analyzing DR. According to [PERSON] et al. [10], DR can be classified into five grades: grade 0 is normal with no sign of DR, grade 1 means the presence of mild DR, grade 2 means moderate, grade 3 means severe, and, finally, grade 4 is defined by new vessel proliferation, where risks of vision loss include bleeding into the vitreous and tractional retinal detachment. Figure 1 shows the different grades of DR. Deep learning belongs to the broad family of machine learning methods [11]. Differently to traditional neural networks-based classifiers, deep learning builds classifiers with many hidden layers, aiming at identifying the salient low-level features of an image [12]. In the context of deep learning, transfer learning is a technique that exploits the usage of features that were learned by a network over a given problem to solve a different problem in the same domain. Transfer learning has many advantages. First, it saves computational time because, instead of training a new model from scratch, it makes use of the information that is already available from the last training process. Second, it extends the knowledge it acquired from previous models, and third, transfer learning is very useful when the size of the new training dataset is small. Transfer learning promises valuable contributions to the fields of computer vision, audio classification, and natural language processing. There have been many attempts to automatize the image classification task--either to facilitate the process or to make it more accurate. One of the earliest attempts was the convolutional neural network (CNN), which was introduced by [13] for the image classification task. In 2012, thanks to the work of [PERSON] et al. [14], CNNs became the most popular technique for addressing the image classification problem. The authors achieved state-of-the-art performance in the ImageNet Large Scale Visual Recognition Challenge (ILSVRC) competition [15], outperforming other commonly used machine learning techniques. CNNs can be used in image classification, as well as natural language processing [16; 17; 18] and time series analysis [19; 20]. In all of these cases, training the weights of the deep network from scratch requires a substantial amount of time and huge datasets (hundreds of thousands of images).