This research proposes a novel method for identifying diabetic retinopathy using retinal fundus images. The current challenge in medical image processing is the time-consuming and skill-intensive manual analysis of retinal fundus images. To address this, the study develops a graphical user interface (GUI) that integrates imaging algorithms to assess whether a patient's fundus image is affected by diabetic retinopathy. The diagnosis is made using a deep neural network, specifically ResNet152-V2, which has demonstrated 100% accuracy in all evaluation criteria, including accuracy, recall, precision, and F1 Score. The severity of the disease is displayed on the GUI, and patient information is stored in a local database. This method can also serve as a backup option for ophthalmologists, reducing the necessary processing time. The study emphasizes the importance of timely identification and treatment of diabetic retinopathy to prevent blindness, especially in affluent nations where it is a leading cause of blindness. The use of deep learning and intelligent systems in clinical applications is highlighted as a promising approach to enhance diagnostic accuracy and efficiency.This research proposes a novel method for identifying diabetic retinopathy using retinal fundus images. The current challenge in medical image processing is the time-consuming and skill-intensive manual analysis of retinal fundus images. To address this, the study develops a graphical user interface (GUI) that integrates imaging algorithms to assess whether a patient's fundus image is affected by diabetic retinopathy. The diagnosis is made using a deep neural network, specifically ResNet152-V2, which has demonstrated 100% accuracy in all evaluation criteria, including accuracy, recall, precision, and F1 Score. The severity of the disease is displayed on the GUI, and patient information is stored in a local database. This method can also serve as a backup option for ophthalmologists, reducing the necessary processing time. The study emphasizes the importance of timely identification and treatment of diabetic retinopathy to prevent blindness, especially in affluent nations where it is a leading cause of blindness. The use of deep learning and intelligent systems in clinical applications is highlighted as a promising approach to enhance diagnostic accuracy and efficiency.