## 2 Background and Previous Work  
Diabetic retinopathy (DR) appears in people with a medical history of diabetes [11] and high blood glucose levels. Many researchers have worked on DR symptom detection using feature recognition techniques [12]. Other methods are based on recognizing the retinal blood vessels and pathologies from fundus images as features and classifying the diabetic retinopathy severity grades [13]. Feature extraction and image analysis for DR classification show great potential for DR grading; however, they excessively depend on labelled data. These methods rely on pixel-level annotation data. This type of annotation is useful in techniques to locate lesions within an image and segment out regions of interest from the background [14]. Image processing techniques using machine-learning methods suffer from the lack of domain experts for validation [15]; in such cases, the dependency on data and statistical models without human experts' validation is still questionable [16]. Currently, deep learning and convolutional neural networks (CNNs) are frequently used in medical applications with computer vision, especially the automated detection of diabetic retinopathy [17; 18]. Extracting important features, such as hard exudates, blood vessels, and texture [19], using a transfer learning-based CNN architecture from fundus images performs relatively well. CNN studies address the grading of non-proliferative DR categories, namely mild, moderate, and severe stages, using a transfer learning-based DR detection system [20], but performance issues face most of the deep learning methods due to the small number of DR fundus images used to train a deep CNN model; hence, overfitting problems appear [21; 22; 23; 24]. Convolutional neural networks (CNNs) are a powerful tool for DR detection, which includes different tasks: classification [25], segmentation [26], and detection [27]. Researchers [28] have coupled CNNs with transfer learning and hyper-parameter tuning, adopted AlexNet, VggNet, GoogleNet, and ResNet, and analyzed how well these models handle DR image classification, using Kaggle datasets to train these models. The best classification accuracy is 95.68% using transfer learning with data augmentation, where the fundus images data were increased to 20 times the original. The authors of [29] (Resnet50, Inceptionv3, Xception, Dense121, Dense169) enhanced the classification of different stages of DR. The experimental results show that the proposed model detects all of the stages of DR. The achieved accuracy is 80.8%. Many hybrid methods based on CNN and other intelligent methods have been proposed, such as the Swarm Optimization (PSO) algorithm-based Convolutional Neural Network (CNN) Model, also called the PSO-CNN model [30], to detect DR from color fundus images. Many proposed hybrid CNN models used preprocessing, feature extraction, and classification [31]. [PERSON] et al. [32] used feature extraction for blood vessels in retinal fundus images using a contour detection algorithm based on fuzzy rules. Fuzzy rules were applied to image gradient values to extract edges and make DR classification decisions based on membership functions. The results of this model offered a similar performance to CNN methods, but it contains flexible rules, offering an alternative to current deep learning applications, severity classification of DR using CNN and attention module proposed in [33], which reduced both the complexity of the model and the training time needed. This research work proposed a method for fine-tuning a pre-trained CNN model for DR grading using fuzzy rules and fundus images. The method takes a retinal fundus image as the input, the CNN model processes it with the fine-tuned model and grades it into normal or DR levels and then the fuzzy system takes the processed images and classifies them based on human experts' rules into four categories (normal, mild, moderate and severe) with the grading percentage.