## 1 Introduction  
Diabetic retinopathy (DR) is a major cause of vision loss worldwide [1]. Regular screening for DR can detect early features or symptoms. However, human experts in this domain still perform the diagnosis. Computer-aided disease diagnosis in retinal image analysis could provide a reasonable solution for the screening and diagnosis processes. Automated tools for clinical stage measurements of retinal problems can provide continuous and accurate monitoring of the disease. Advances in artificial intelligence and machine learning approaches enable such application for clinical practice [2].  
Recently, an important need has arisen for the automation of an accurate DR detection system, as providing an affordable, accurate system will overcome the problem of a lack of retina specialists around the word [3]. The detection of different patterns in retinal images is a key factor in DR measurement. Retinal tissue in diabetic patients deforms in different ways, such as microaneurysms that appear as tiny red dots on images of early-stage DR. Microaneurysms usually grow into retinal hemorrhages in moderate DR cases, and in some cases yellow or white exudates can observed, while in severe cases, blood vessels leak into the retina, known as macular edema, causing blurry vision [4].  
Intelligent health care application has an important function in retinal image analysis and feature extraction, and provides a reasonable solution to control the growth of DR, especially if the detection occurs at the early stages; moreover, deep learning methods for DRgrading have achieved significantly improved performance [5]. However, accurate classification for DR grading remains challenging due to many reasons, such as the insufficiency of training samples and the poor quality of fundus images taken using different devices [6]. The measurement of the DR severity stage is a difficult task due to the differences in the sizes of lesions among fundus images of the same class and visual similarities in detected features, such as shapes and colors between the fundus images of different classes [7].  
Hybrid artificial intelligence approaches face performance challenges in CNN models. [PERSON] et al. [8] proposed a Hybrid Graph Convolutional Network (HGCN) for diabetic retinopathy grading with limited labeled data and a large amount of unlabeled data (semi-supervised learning), and the experimental results showed the better performance of HGCN in semi-supervised retinal image classification.  
Considering all of these facts, the proposed work implemented a simple CNN for feature extraction and combined it with a rule-based expert system for better classification using the model used in [9]; the model was trained using Kaggle public datasets [10], and the rules changed based on the new domain of application. The grading of diabetic retinopathy was implemented without segmentation; furthermore, the framework gives similar performance for all classes. This study provides important contributions to this field by enhancing the accuracy and the performance based on a hybrid approach and a relatively small amount of data.  
The goal of the proposed hybrid intelligent framework consisting of a conventional neural network and a fuzzy inference system to efficiently measure the stages of DR.  
* The framework has similar performance for all classes, overcoming the problem of different data sizes in each training class;
* The framework does not need a segmentation phase;
* The framework adds a rule-based system based on human experts' knowledge to the deep learning model;
* The evaluation and comparison with related models show that the framework outperforms the other models.  
The rest of this paper is organized as follows: Section 2 provides the background and previous works. In Section 3, we present the research methodology in detail. Section 4 shows the evaluation and experimental results. Finally, in Section 5, we conclude our work and identify future work avenues.