# Diabetic Retinopathy Diagnosis Based on RA-EfficientNet
###### Abstract
Deep learning has become a research hotspot in the medical field, and its development has effectively promoted the progress of DR research. [PERSON] et al. [9] used ResNet and a decision tree classifier to distinguish between sick and healthy images, and the AUC for the Messidor dataset is up to 0.94. [PERSON] et al. [10] used EfficientNet combined with transfer learning to detect referable diabetic retinopathy (RDR) and vision-threatening DR (VTDR), which obtained satisfying results of up to 0.98 AUC both for the APTOS 2019 dataset and EyePACS dataset. [PERSON] et al. [11] employed pre-trained ResNet50 to achieve 96.59% accuracy for an APTOS 2019 dataset for the binary classification task, that is to detect No-DR or DR. The above classification models performed well, however they are of binary classification, meaning that it is unable to make an in-depth classification of specific diseases. Therefore, it is necessary to further study the grade classification of DR. [PERSON] et al. [12], based on the Messidor dataset, designed a neural network to automatically classify normal images, for stage 1, stage 2, and stage 3 of DR, with an accuracy up to 96% for each grade. This algorithm realizes four classifications of DR, that can better reflect the severity of DR than the use of two classifications. In recent years, the five classifications of DR have attracted greater attention because they are able to better reflect the DR and its severity levels. [PERSON] et al. [13], based on the APTOS 2019 dataset, combined the pre-training model NASNET with the T-SNE space to extract deep features, achieving an accuracy rate of 77.90%. [PERSON] et al. [14] proposed a composite deep neural network of Xception and VGG16 with gated attention mechanism to automatically diagnose DR. The accuracy of this model on the APTOS 2019 dataset was 82.54%. [PERSON] et al. [15] combined the Xception model with a regression model to classify the five stages of DR, achieving an accuracy of 82%, 86% for the EyePACS and APTOS 2019 datasets respectively. [PERSON] et al. [16] used the pre-trained MobileNetV2 to classify the DR of the APTOS 2019 dataset, and obtained an accuracy of 91%. With the development of deep learning, the ability of a network to extract DR features has been enhanced, and the accuracy has been improved to some extent. However, for clinical diagnosis, the accuracy of a DR severity classification still deserves further improvement. From the above statements, we can see that, firstly, in the diagnosis of DR, deep learning methods have received more attention for their improved performance; secondly, the grade classification for different severity levels of DR is easier for doctors to interpret; thirdly, the deep learning networks combined with transfer learning technology can provide more accurate results, especially with regard to the latest algorithms. Therefore, a simple and efficient network is required for a more accurate and effective diagnosis of DR of different severities. Based on the above analysis, we constructed a DR grade diagnostic model in this paper. The steps performed for the construction of this model are summarized as follows: firstly, to reduce the inclusion of redundant information and to make the APTOS 2019 dataset more suitable for the diagnosis model, pre-processing steps are adopted. Secondly, to extract the features of DR images more accurately and efficiently, we propose the creation of a new network named RA-EfficientNet for the feature extractor of the model, based on the combination of EfficientNet and the residual attention block i.e., RA block. Finally, according to classification tasks, we designed two classifiers, one of which is a 2-grade classifier used for the identification of DR, and the other is a 5-grade classifier for the diagnosis of DR with respect to severity levels.