## 1 Introduction
stated that the accuracy of the software could be improved by including additional image processing techniques or other methods based on artificial intelligence (AI) or deep learning (DL), although they did not explore this further [11]. Another study carried out by [PERSON] et al. provided an effective image processing method for DR detection from retinal fundus images [12]. They presented a system that could convert red, green, and blue (RGB) images into hue, saturation, and intensity (HSI) images for processing and feature extraction. The main goal of feature extraction was to identify micro-aneuryss, exudates, and hemorrhages and then perform classification based on them. [PERSON] et al. used machine learning (ML) algorithms such as a support vector machine (SVM) [13], a probabilistic neural network (PNN), and a support vector machine optimized with a genetic algorithm (SVMGA) for classification [12]. In order to evaluate the results, they compared the sensitivity, specificity, and accuracy of the three methods used. This showed that SVMGA was equal to or better than SVM and PNN. The first step that they applied was fundus image normalization to improve the quality of the images. The second step involved feature extraction from general fundus images; these were divided into the following three features: optic disks, fovea, and vessel structures. In the third step, algorithms were developed to cover DR pathologies corresponding to the severity classification. The proposed algorithms were separated according to the structure of each pathology in the fundus image. The last step was the determination of the severity of the DR detected [12]. Moreover, the studies carried out by [PERSON] et al. and [PERSON] et al. examined the discrimination of DR using ML methods in optical coherence tomography angiography (OCTA) images [14] and optical coherence tomography (OCT) images [15], respectively. [PERSON] et al. considered the evaluation of OCTA to discriminate between DR and healthy controls (HC) using 144 images [14]. Four ML models, namely logistic regression (LR) [14; 16], logistic regression regularized with an elastic net (LR-EN) penalty, SVM, and the gradient boosting tree (XGBoost), were used to classify wavelet features between groups. OCTA data consisting of the superficial vascular plexus, deep vascular plexus, and retinal vascular network were acquired from 19 DR (38 eyes) patients and 25 HC (44 eyes). A discrete wavelet transform was applied to extract texture features from each image. The area under the curve (AUC) of the receiver operating characteristic (ROC) curve, sensitivity, specificity, and accuracy of the network. The results of the experiments were summarized in Table 1.  
The results of the experiments are summarized in Table 2. The results of the experiments are summarized in Table 3. The results of the experiments are summarized in Table 4. The results of the experiments are summarized in Table 5. The results of the experiments are summarized in Table 6. The results of the experiments are summarized in Table 7. The results of the experiments are summarized in Figure 1:0 The results of the experiments are summarized in Figure 1:1 The results of the experiments are summarized in Figure 1:2 The results of the experiments are summarized in Figure 1:3 The results of the experiments are summarized in Figure 1:4 The results of the experiments are summarized in Figure 1:5 The results of the experiments are summarized in Figure 1:6 The results of the experiments are summarized in Figure 1:7 The results of the experiments are summarized in Figure 1:8 The results of the experiments are summarized in Figure 1:9 The results of the experiments are summarized in Tableand diagnostic accuracy of the classifiers were obtained. The results showed that the LR-EN algorithm had high sensitivity and specificity in identifying DR, indicating that it may be a promising method in facilitating the early diagnosis of DR. However, the used database was insufficient, as it only comprised 144 images.