## 5 Conclusions and Future Work  
This work involved the exploration of different types of DL algorithms in order to automatically detect DR from fundus images. We utilized eight networks and assessed their sensitivity, specificity, accuracy, and precision in detecting DR. The results indicated that DL-based approaches are effective in detecting DR. The highest sensitivity was obtained using GoogleNet, which exhibited 99.44% sensitivity in DR detection, i.e., almost 100%, while ResNet-50 exhibited the highest specificity and precision at 89.74% and 90.7%, respectively. Moreover, both GoogleNet and ResNet were 93.56% accurate in detecting DR, while the other networks exhibited similar scores. Although there are numerous classification networks available in the literature, the studied algorithms were selected based on previous research focusing on disease detection from images. The current work focused on exploring the significance of the different types of images and datasets. Significant results were obtained due to the use of a combination of medical, clinical, computational, and statistical evaluations, aiming to contribute to improving healthcare services. Moreover, we determined the optimal network by taking into consideration the demographics and types of images. We summarize the main outcomes and achievements as follows:  
* We provided a new process and a new level of diagnosis for patients with DR, thus paving the way for enhanced healthcare services;
* We explored the most powerful DL algorithms in the detection of this particular disease and in a specific population for preventative purposes;
* At the clinical level, we developed an \"augmented diagnosis\";
* At the medical level, we highlighted the significance of using specific types of images, mainly fundus images with DNNs and functional images with ML or combined ML, where the DNNs showed significant accuracy;
* At the computer level, we developed an autonomous and automatic evaluation;
* At the AI level, we combined algorithms presented in earlier research with the currently used DL algorithms, as well as exploring existing DL networks and optimizing their parameters;
* At the statistical and evaluation level, we achieved the highest accuracy in diagnosis and calculated the error rate in DR diagnosis for the first time;
* We devised a framework that resulted in the minimum error in automatic diagnosis and early detection, with results that were in accordance with the clinical findings. Lastly, the results obtained through the new framework's application to medical images show that it is a promising tool for the diagnosis of DR, surpassing those found in the literature. As DR is the leading cause of avoidable vision impairment worldwide, the automatic early detection and diagnosis of DR, enabled by our framework, plays a vital role in preventing blindness or other types of visual impairment. The automatic detection of DR could enable doctors to quickly diagnose the disease, assist specialists in saving time and money, and benefit patients in terms of preventing the onset of blindness. As a future direction, the development of a grading algorithm to further improve the application of this framework should be considered. Certain networks, such as VisionTransformer, Swin Transformer, ConvNeXt, and Vision Mamba, could also be considered in future research. Moreover, while the construction of a new network is an important innovation, it must exhibit promising outcomes. Our framework can also be applied to different types of eye images and different population demographics. Regarding the aspects of DR that were not explored, additional work linking the diagnosis to the factors behind the disease must be carried out. Moreover, additional metrics and scores, such as the AUC and boxplots to demonstrate the standard deviation, could be considered for the evaluation of DR diagnoses, thus paving the way for enhanced healthcare services.