## 1 Introduction  
Diabetic retinopathy can cause non-reversible damage to retina blood vessels and even blindness. The risk can be reduced by early diagnosis. However, scanning is an intensive task. Hence computer-aided scanning models of diabetic retinopathy are necessary. Many machine learning algorithms have been proposed to build an automatic classifier of diabetic retinopathy, which is the core of a computer-aided scanning system. In machine learning methods, the data are the basis for learning the classifiers for diabetic retinopathy. A diabetic retinopathy dataset was provided on the Kaggle website in 2015 [1]. The Kaggle diabetic retinopathy detection competition consists of 35,126 and 53,576 fundus images for training and testing, respectively. [PERSON] et al. [2] collected a fundus image dataset for the Indian population. The Indian Diabetic Retinopathy Image Dataset (IDRiD) consists of 516 fundus images. [PERSON] et al. [3] collected a fundus image dataset for the Chinese population. The dataset is named DDR (Dataset of Diabetic Retinopathy), and it consists of 13,673 color fundus images. [PERSON] et al. [4] constructed a diabetic retinopathy dataset named FGADR. FGADR contains 2842 fine-grained annotated diabetic retinopathy images. [PERSON] et al. [5] published a diabetic retinopathy dataset named Messidor in 2008. The Messidor dataset contains 540 normal images (fundus images without diabetic retinopathy) and 660 abnormal images (fundus images with diabetic retinopathy). Supported by the labeled fundus images, many automatic classification models of diabetic retinopathy have been built by deep learning methods and have achieved state-of-the-art performance. [PERSON] et al. [6] showed that deep neural networks achieved good performance for the detection of diabetic retinopathy. The performances of different deep learning networks,such as VGG19, InceptionV3, ResNet50, NASNet, and MobileNet, were tested for the classification of diabetic retinopathy in [7]. [PERSON] et al. [8] built two deep learning models for diabetic retinopathy; one was applied to distinguish fundus images, and the other was used to detect the lesions of diabetic retinopathy. Transfer learning can be used to improve the classification model for diabetic retinopathy [9], and it is considered as a useful method for learning from a dataset with a limited size [10]. In essence, a deep learning method requires a sufficiently large number of data to obtain a reliable model. It is risky to obtain a robust classifier for a deep learning model trained on a dataset with a limited size. [PERSON] et al. [11] suggested that the ordinal information of classes is helpful. Hence, an ordinal regression method was applied to train a more discriminative classification model for diabetic retinopathy. The new deep learning network called BiRA-Net was proposed in [12]. An attention mechanism was applied to build a fine-grained classification model. Common deep learning networks lack explanation and are sensitive to the training parameters. The explanation and robustness should be improved to build a more practical classification model for diabetic retinopathy. [PERSON] et al. [13] proposed an eXplainable Artificial Intelligence (XAI) for the classification task of diabetic retinopathy. Independent component analysis with a score visualization technique was used to generate an explainable deep learning model for diabetic retinopathy [14]. The visualization strategy of networks was applied to discover the inherent features of fundus images in [15]. For a practical medical model, the robustness of the model is a key factor. Adversarial training methods were applied to improve the robustness of the classification model in [16]. The built classification model should not fail due to minor changes in the fundus images. Spatial attention and channel attention mechanisms were considered in [17] to capture the important features from fundus images and finally build a robust classification model.