## 1 Introduction
Detecting DR at an early stage is crucial for identifying the disease before its progression to an advanced state, characterized by problems such as vision loss, as early intervention allows for the implementation of suitable treatment methods. In this context, patients with diabetes are required to undergo annual or biannual retina monitoring [2, 29]. Additionally, as stated by the National Eye Institute, up to 95% of instances of vision loss can be avoided through the early detection and treatment of DR [29]. The main goal that motivated the present research was the early detection of DR automatically from fundus images. In addition, any method for DR detection must provide adequate results to be considered efficient, i.e., the algorithm must have high accuracy and a low rate of false detection. Based on the above, our research aimed to develop a framework that could detect DR automatically based on DL and based on the evaluation of a retrospective, controlled clinical database. This could improve the quality of life of patients with DR and improve the provision of healthcare in this context. This could be achieved by training pre-existing NNs, employing DL algorithms, and evaluating the results quantitatively through sensitivity, specificity, accuracy, and precision metrics, as well as calculating the cross-entropy and loss or error in DR diagnosis. Qualitatively, ROC curves could be used to assess the performance of different algorithms. Although our aim was partly to explore existing networks, several classical and combined networks were presented in an earlier research work [28], and this was extended in the current work based on a thorough review of the topic. One of the novelties is that this work explores \"new processes\" and/or \"new levels\" of diagnosis for patients with DR, thus enhancing the related healthcare services. Another novelty is the discussion of augmented and autonomous clinical decision making (augmented diagnosis), in addition to exploring the power of DL algorithms in the detection of this particular disease (DR) for preventative purposes (to avoid blindness). Moreover, we shed light on the importance of the types of images used, and we target automatic and optimal outputs and minimal errors. Furthermore, at the AI level, we explore the currently used DL algorithms and the optimization of their parameters, as well as calculating the error in DR detection. Meanwhile, at the machine level, we provide a framework that could be integrated with the software of the digital slit lamp in the lab to automate the diagnostic procedure, thus providing an AI-integrated digital slit lamp that could enhance the healthcare system. This work is divided into five main sections. After introducing the research context and recent studies related to DR detection and diagnosis, Section 2 describes the experimental materials and methods used, including the collection of the experimental data, a priori information, features, classifiers, and the evaluation method and metrics used. Section 3 presents the results, which are divided into quantitative and qualitative results. Then, Section 4 discusses these results. Finally, Section 5 provides the conclusions and recommends future research directions that can be considered to promote the automatic diagnosis of DR.