# Classification of Diabetic Retinopathy Based on Efficient Computational Modeling
## 2 Related Work  
The World Health Organization's first-ever World Vision Report revealed that at least 2.2 billion people worldwide are visually impaired or blind, and that the majority of visual impairment and blindness can be avoided through early prevention [27]. In diabetic fundus screening, professional ophthalmologists will classify the degree of retinopathy of the patient based on the characteristics of the blood vessels in combination with other diseased areas, and will take appropriate measures to reduce the risk of blindness. Due to the large number of patients around the world and the tiny size of the blood vessels and lesions in the retina, doctors in some underdeveloped areas may make misdiagnoses and omissions during the diagnostic process. In early DR classification tasks, machine learning methods were generally used, which required experienced physicians to manually annotate the lesion features and then make judgments based on the manually extracted features, which were more dependent on the results of the feature extraction approach and did not address the problem of the high cost of manual diagnosis and there were no publicly available retinal datasets in the early days, so there were fewer studies that used machine learning methods. In the past studies, [PERSON] [28] used support vector machines to achieve the classification of lesions and the degree of higher-order spectral techniques. [PERSON] et al. [29] used the K-nearest neighbor algorithm (KNN) to classify diabetic retinopathy and proposed a traditional machine learning framework based on image processing and feature extraction. [PERSON] and [PERSON] [30] used a morphological approach to extract pathological features, and then completed the ratings of the degree of retinopathy by using support vector machine classifiers. [PERSON] et al. [31] mapped basic anatomical features and pathological changes by fusing feature information from different scanned laser fundus images, and then used a support vector machine to classify the patient's lesions into three categories. [PERSON] et al. [32] processed each fundus image using various linear and nonlinear image filtering algorithms to generate feature data, and then used these feature data to train a random forest classifier to achieve classification. With the development of deep learning technology, the amount of data is also growing under the processing of neural networks. CNN has a great advantage in processing high-dimensional information such as images. The Kaggle competition platform released large-scale retinopathy datasets EyePACS and APTOS in 2015 and 2019, respectively. Since then, the use of deep learning to classify diabetic retinopathy has become a mainstream method. Compared with traditional machine learning based methods, the biggest advantage of using deep learning technology to achieve automatic segmentation and DR classification of retinal blood vessels is that it does not require manual feature extraction. Deep models autonomously learn the intrinsic connections between features, avoiding the influence of subjective factors on result judgments. In addition, automatic segmentation and classification based on deep learning greatly reduce the consumption of related manpower and resources, improve the efficiency of disease screening, and alleviate the internal contradiction of the increasing number of ophthalmic diseases and the shortage of professional physicians. Therefore, many CNN-based DR classification models have emerged in subsequent research. Earlier work generally performed DR binary classification studies, such as the work of [PERSON] et al. [33] based on transfer learning technique and InceptionV3 model and the explicit algorithm for lesion region designed based on visualization method by [PERSON] et al. [34]. In fact, the severity classification of DR can be more helpful for doctors' clinical diagnosis and judgment, so more researchers carry out five classification studies. In terms of model structure, a 10-layer CNN designed by [PERSON] et al.