## 1 Introduction
Other large-scale and multicenter studies have been carried out to assess the applicability of the LR-EN algorithm in DR and related eye diseases, seeking to improve the vision-related outcomes of patients [17; 18; 19; 20]. Although previous studies have employed DL algorithms, some have focused on OCTA, others on fundus images, and some have focused specifically on detecting non-proliferative DR. Moreover, in 2023, [PERSON] et al. proposed a novel architecture combining ResNet-50 with a channel attention squeeze-and-excitation network (SENet), designed to extract features, and they introduced a disease attention module to supplement disease-specific information about diabetic macular edema (DME) [21]. DME is a specific complication of DR in which fluid accumulates in the macula, the central part of the retina that is responsible for sharp, central vision. However, DR is wide-ranging and is considered a general condition caused by damage to the blood vessels in the retina due to prolonged high blood sugar levels in diabetes. Thus, any study that explores DR will include DME, but the opposite is not true; i.e., studying only DME limits the cases that can be obtained by imaging, and the findings would be specific to DME rather than generalizable across all cases of DR. In addition, [PERSON] et al. presented a novel approach to early DR detection based on multifractal geometry analysis and SVM, focusing on OCTA macular images [22; 23]. Their model involved using a supervised ML method, such as the SVM algorithm, on 170 eye images from patients with early-stage DR, which were divided into 90 healthy eyes and 80 eyes. These were obtained from the Ophthalmology Center at Mansoura University, Egypt. The training procedure involved using seven extracted features as the training dataset. The extracted features were specific to the two classified stages, indicating that they were a good choice for the classification process. These features were the \(\alpha\) at the maximum of the singularity spectrum (\(\alpha\)), the shift in the singularity spectrum's symmetrical axis, the width of the singularity spectrum (W), the lacunarity, the dimension of box counting (DB), the dimension of information (DI), and the dimension of correlation (DC). These generalized features reflect the self-similarity, morphological characteristics, and pixel correlations. Furthermore, the objective was to classify the input data images into one of two classes, namely normal and non-proliferative diabetic retinopathy (NPDR). Classification was performed using an SVM classifier with a radial basis function (RBF) kernel. The authors showed that their proposed technique achieved the best performance in the early detection of DR compared to the others [22; 23]. These results could be enhanced by increasing the training dataset [23; 24; 25; 26; 27]. Furthermore, [PERSON] et al. adopted AI to diagnose DR automatically from optical coherence tomography angiography (OCTA) images [28]. Their research focused on providing an adequate ML and DL technique to differentiate between normal images and images of patients with DR, using OCTA images obtained from 90 patients [28]. They used data collected prospectively over the course of a year from a comprehensive medical center in Lebanon. The main algorithms were the mixed convolutional neural network (CNN, SVM) algorithm, the feed-forward backpropagation NN, SVM with a linear kernel, and SVM with a polynomial kernel. The results showed that the proposed combination (CNN, SVM) offered the best detection as compared to the regular SVM, the polynomial-based SVM, and the NN [28]. However, the authors discussed the importance of employing more algorithms--specifically, pure DL algorithms--and comparing the results to those extracted from fundus images to enable the early detection and diagnosis of DR [28].