  Diabetic retinopathy(DR) is the main cause of blindness in diabetic patients.
However, DR can easily delay the occurrence of blindness through the diagnosis
of the fundus. In view of the reality, it is difficult to collect a large
amount of diabetic retina data in clinical practice. This paper proposes a
few-shot learning model of a deep residual network based on Earth Mover's
Distance algorithm to assist in diagnosing DR. We build training and validation
classification tasks for few-shot learning based on 39 categories of 1000
sample data, train deep residual networks, and obtain experience maximization
pre-training models. Based on the weights of the pre-trained model, the Earth
Mover's Distance algorithm calculates the distance between the images, obtains
the similarity between the images, and changes the model's parameters to
improve the accuracy of the training model. Finally, the experimental
construction of the small sample classification task of the test set to
optimize the model further, and finally, an accuracy of 93.5667% on the
3way10shot task of the diabetic retina test set. For the experimental code and
results, please refer to:
https://github.com/panliangrui/few-shot-learning-funds.
