# Diabetic Retinopathy Diagnosis Based on RA-EfficientNet
###### Abstract  
The early detection and grade diagnosis of diabetic retinopathy (DR) are very important for the avoidance of blindness, and using deep learning methods to automatically diagnose DR has attracted great attention. However, the small amount of DR data limits its application. To automatically learn the disease's features and detect DR more accurately, we constructed a DR grade diagnostic model. To realize the model, the authors performed the following steps: firstly, we preprocess the DR images to solve the existing problems in an APTOS 2019 dataset, such as size difference, information redundancy and the data imbalance. Secondly, to extract more valid image features, a new network named RA-EfficientNet is proposed, in which a residual attention (RA) block is added to EfficientNet to extract more features and to solve the problem of small differences between lesions. EfficientNet has been previously trained on the ImageNet dataset, based on transfer learning technology, to overcome the small sample size problem of DR. Lastly, based on the extracted features, two classifiers are designed, one is a 2-grade classifier and the other a 5-grade classifier. The 2-grade classifier can diagnose DR, and the 5-grade classifier provides 5 grades of diagnosis for DR, as follows: 0 for No DR, 1 for mild DR, 2 for moderate, 3 for severe and 4 for proliferative DR. Experiments show that our proposed RA-EfficientNet can achieve better performance, with an accuracy value of 98.36% and a kappa score of 96.72% in a 2-grade classification and an accuracy value of 93.55% and a kappa score of 91.93% in a 5-grade classification. The results indicate that the proposed model effectively improves DR detection efficiency and resolves the existing limitation of manual feature extraction. EfficientNet; transfer learning; residual attention block; retinal image; diabetic retinopathy +
Footnote †: journal: Medicalvision or even complete blindness. Since the characteristics of diabetic retinopathy are complex and diverse, finding the corresponding features of the disease is a challenge. At present, in clinical practice, doctors diagnose DR using fundus photograph, which is still a time-consuming artificial method. However, the increasing number of DR patients and the scarcity of high-level ophthalmology experts will likely result in a missed diagnosis, misdiagnosis alongside other problems. Computer aided diagnosis (CAD) does not present the problem of manual diagnosis, which greatly reduces the workload and the time spent by doctors in diagnosing diseases, and provides high accuracy. Recently, deep learning has led to great progress and made valuable research and application contributions in the field of CAD analysis. A Convolution Neural Network (CNN) is most effective in the field of computer vision for its excellent performance in image classification tasks. In recent years, scholars have proposed multiple CNN algorithms, such as VGGNet [1], GoogLeNet [2] and ResNet [3], however, as a routine operation in these works, they usually only scale one of the three dimensions-depth, width and resolution, which often leads to poor accuracy and efficiency. EfficientNet [4] demonstrates better performance by uniformly scaling depth, width and resolution, and this algorithm provides a new research direction for the subsequent development of CNN. The development of CNN has achieved revolutionized progress in medical areas, such as retinal vascular segmentation [5], glaucoma screening [6], cancer subtype classification [7] and so on. The application of these CNN architectures is well suited for medical classification tasks, nevertheless, the problem of insufficient medical data has emerged, which makes the model's training challenging. Transfer learning [8] is a technique which, to ensure that the network is well trained, transfers information from the pre-trained dataset, which includes a huge number of images, to a new dataset. It is an excellent tool for the task of making the network more efficient and stable based on insufficient data.