Introduction The world is currently witnessing a significant increase in the number of people diagnosed with diabetes, making it one of the major health challenges facing healthcare systems in various countries. The danger of this disease is not limited to elevated blood glucose levels; it also extends to numerous chronic complications that affect different organs of the body. Diabetic retinopathy is considered one of the most serious of these complications, as it directly affects the eye and may lead to partial or complete vision loss if it is not detected and treated in its early stages. The retina contains a delicate network of blood vessels that supply it with oxygen and essential nutrients required for proper visual function. When blood glucose levels remain elevated for prolonged periods, these blood vessels gradually become damaged, leading to various pathological changes, including bleeding, swelling, blood vessel blockage, and the growth of abnormal blood vessels. These changes are among the main indicators of diabetic retinopathy. In previous decades, physicians primarily relied on clinical examinations and the manual analysis of fundus images to detect the disease. This process requires considerable expertise in ophthalmology, as physicians examine images and search for pathological signs that may be extremely small or distributed across different regions of the retina. Although this approach is important and effective, it can be time-consuming and depends heavily on the examiner’s experience, potentially leading to variations in diagnostic results among different physicians. The Importance of Early Detection Early detection of diabetic retinopathy is one of the most important factors in preserving vision and preventing the disease from progressing to advanced stages. The earlier the condition is identified, the greater the chances of successful treatment and the lower the likelihood of serious complications that may result in permanent vision loss. The main challenge is that the early stages of the disease are often not accompanied by noticeable symptoms, causing many cases to remain undetected. By the time symptoms begin to appear, retinal damage may have progressed to a stage at which fully restoring visual function becomes difficult. Therefore, healthcare institutions and research organizations are working to develop efficient and rapid screening methods that can be widely implemented to monitor people with diabetes on a regular basis. The Role of Artificial Intelligence in Healthcare In recent years, artificial intelligence applications in healthcare have advanced significantly. Machine learning and deep learning techniques are now being used in numerous fields, including disease diagnosis, medical image analysis, tumor detection, skin disease classification, and patient monitoring. Medical image analysis is considered one of the fields in which artificial intelligence has achieved remarkable progress, owing to the ability of modern algorithms to recognize subtle patterns and extract important features from images with high efficiency. These developments have contributed to the creation of intelligent systems capable of assisting physicians in making medical decisions more quickly and accurately. In the case of diabetic retinopathy, artificial intelligence can analyze fundus images and automatically detect pathological signs, such as microaneurysms, retinal hemorrhages, lipid exudates, swelling, and various vascular abnormalities. In this way, the system becomes more than a simple image analysis tool; it serves as a medical aid that contributes to early disease detection and reduces the workload of healthcare professionals. The Process of Fundus Image Analysis Automated detection of diabetic retinopathy involves several interconnected stages. The process begins with acquiring fundus images using specialized cameras that capture clear images of the retina and its blood vessels. These images are then stored in medical databases for use in model training or diagnostic screening. The next stage is image preprocessing, which is essential for improving the quality of the data provided to the intelligent system. This stage includes reducing noise caused by the imaging process, enhancing contrast, adjusting illumination, and standardizing image dimensions. These procedures help highlight the fine details within the retina and make them more distinguishable to the analytical system. The images then proceed to the stage of feature extraction and identification of relevant characteristics. In traditional systems, these features were manually defined by researchers. In modern deep learning systems, however, neural networks automatically learn and extract these features during the training process. One of the most widely used models in this field is the Convolutional Neural Network (CNN), which has demonstrated strong capabilities in medical image analysis. These networks learn to identify differences between healthy and affected eyes by analyzing thousands of previously labeled images. Almustaqbal University – The First University in Iraq