Abstract: To solve the problems that the identification of diabetic retinopathy mainly depends on the clinical experience of doctors and the features of the lesions are difficult to be distinguished by eyes and the recognition rate is low, a diabetic retinopathy classification method based on attention neural network is proposed. Firstly, the retinal images are preprocessed by normalization, histogram equalization and data enhancement. Secondly, 2-DenseNet is proposed by adjusting the classical DenseNet to reduce the number of connections on the premise of avoiding gradient disappearance and ensuring classification accuracy.At the same time, the attention module is embedded into the network to direct it to focus on features such as exudates, thick blood vessels, and microaneurysms in retinal images, which is used to train and test the pre-processed images. Finally,  multiple models are compared on the public Kaggle dataset, and the experimental results show that the network has a higher classification accuracy for diabetic retinopathy than other models.

Key words: convolutional neural network, diabetic retinopathy, attention mechanism, DenseNet