This research proposes a new method for identifying diabetic retinopathy using retinal fundus images. The method involves a graphical user interface 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 achieved 100% accuracy in all evaluation criteria, including accuracy, recall, precision, and F1 Score. The severity of the disease is displayed on the graphical user interface, and the patient's information is stored in a local database. This method can serve as a backup option for ophthalmologists to support in-disease detection, reducing the necessary processing time. Diabetic retinopathy (DR) is a significant complication associated with elevated blood glucose levels in individuals with diabetes. It can lead to sudden vision loss and is a leading cause of blindness, especially in affluent nations. Timely identification and treatment are crucial for preventing blindness or mitigating the progression of DR into blindness. Comprehensive screening of diabetic patients is highly recommended. Despite extensive work on establishing accurate computerized scanning systems based on color fundus images, detecting diabetic retinopathy from computerized image data remains a challenge. The next step is to apply intelligent diagnosis systems in addition to scanning and artificial vision-based solutions. Artificially intelligent approaches, particularly deep learning, are commonly used in modern life to reduce effort or cost while achieving superior results. The mathematical nature of our environment has enabled intelligent systems to adapt to many difficulties. Smart systems have been implemented in clinical areas, and disease detection is a significant study interest in this method. Computer-assisted intelligent systems are used to reduce the amount of human effort required for basic diagnostic and distinguishing between diabetes and normal patients. Doctors often look for certain symptoms, and a similar scheme can be used by putting variables into different machine learning approaches. A machine learning-based program can be trained to determine if a person is diabetic or not. There are numerous apps for this purpose, and the most recent trend is to apply deep learning. It is also an essential way to apply image processing to clinical imaging data for diagnosis. Several researchers have used transfer learning models and convolutional neural networks (CNN) for the diagnosis of some diseases, such as COVID-19.This research proposes a new method for identifying diabetic retinopathy using retinal fundus images. The method involves a graphical user interface 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 achieved 100% accuracy in all evaluation criteria, including accuracy, recall, precision, and F1 Score. The severity of the disease is displayed on the graphical user interface, and the patient's information is stored in a local database. This method can serve as a backup option for ophthalmologists to support in-disease detection, reducing the necessary processing time. Diabetic retinopathy (DR) is a significant complication associated with elevated blood glucose levels in individuals with diabetes. It can lead to sudden vision loss and is a leading cause of blindness, especially in affluent nations. Timely identification and treatment are crucial for preventing blindness or mitigating the progression of DR into blindness. Comprehensive screening of diabetic patients is highly recommended. Despite extensive work on establishing accurate computerized scanning systems based on color fundus images, detecting diabetic retinopathy from computerized image data remains a challenge. The next step is to apply intelligent diagnosis systems in addition to scanning and artificial vision-based solutions. Artificially intelligent approaches, particularly deep learning, are commonly used in modern life to reduce effort or cost while achieving superior results. The mathematical nature of our environment has enabled intelligent systems to adapt to many difficulties. Smart systems have been implemented in clinical areas, and disease detection is a significant study interest in this method. Computer-assisted intelligent systems are used to reduce the amount of human effort required for basic diagnostic and distinguishing between diabetes and normal patients. Doctors often look for certain symptoms, and a similar scheme can be used by putting variables into different machine learning approaches. A machine learning-based program can be trained to determine if a person is diabetic or not. There are numerous apps for this purpose, and the most recent trend is to apply deep learning. It is also an essential way to apply image processing to clinical imaging data for diagnosis. Several researchers have used transfer learning models and convolutional neural networks (CNN) for the diagnosis of some diseases, such as COVID-19.