Diabetic retinopathy (DR) is a growing health problem worldwide and is a leading cause of visual impairment and blindness, especially among working people aged 20-65. Its incidence is increasing along with the number of diabetes cases, and it is more common in developed countries than in developing countries. Recent research in the field of diabetic retinopathy diagnosis is using advanced technologies, such as analysis of images obtained by ophthalmoscopy. Automatic methods for analyzing eye images based on neural networks, deep learning and image analysis algorithms can improve the efficiency of diagnosis. This paper describes an automatic DR diagnosis method that includes processing and analysis of ophthalmoscopic images of the eye. It uses morphological algorithms to identify the optic disc and lesions characteristic of DR, such as microaneurysms, hemorrhages and exudates. Automated DR diagnosis has the potential to improve the efficiency of early detection of this disease and contribute to reducing the number of cases of diabetes-related visual impairment. The final step was to create an application with a graphical user interface that allowed retinal images taken at cooperating ophthalmology offices to be uploaded to the server. These images were then analyzed using a developed algorithm to make a diagnosis.

翻译：糖尿病视网膜病变（DR）是全球日益严重的健康问题，也是导致视力损伤和失明的主要原因，尤其影响20-65岁的劳动人群。其发病率随着糖尿病病例数量的增加而上升，且在发达国家比发展中国家更为常见。近年来，糖尿病视网膜病变诊断领域的研究正采用先进技术，例如通过眼底镜检查获取的图像分析。基于神经网络、深度学习及图像分析算法的眼图像自动分析方法可提高诊断效率。本文描述了一种自动DR诊断方法，包括对眼底图像的预处理与分析。该方法采用形态学算法识别视盘及DR特征性病变（如微动脉瘤、出血和渗出物）。自动化DR诊断有望提高该疾病早期检测的效率，并有助于减少糖尿病相关视力损伤的病例数量。最终步骤是创建一个具有图形用户界面的应用程序，该程序可将合作眼科诊所拍摄的视网膜图像上传至服务器，随后利用所开发的算法对这些图像进行分析并作出诊断。