Many people are affected by diabetes around the world. This disease may have type 1 and 2. Diabetes brings with it several complications including diabetic retinopathy, which is a disease that if not treated correctly can lead to irreversible damage in the patient's vision. The earlier it is detected, the better the chances that the patient will not lose vision. Methods of automating manual procedures are currently in evidence and the diagnostic process for retinopathy is manual with the physician analyzing the patient's retina on the monitor. The practice of image recognition can aid this detection by recognizing Diabetic Retinopathy patterns and comparing it with the patient's retina in diagnosis. This method can also assist in the act of telemedicine, in which people without access to the exam can benefit from the diagnosis provided by the application. The application development took place through convolutional neural networks, which do digital image processing analyzing each image pixel. The use of VGG-16 as a pre-trained model to the application basis was very useful and the final model accuracy was 82%.

中文翻译：

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通过视网膜图像识别检测糖尿病视网膜病变

全世界有许多人受到糖尿病的影响。这种疾病可能有 1 型和 2 型。糖尿病会带来多种并发症，包括糖尿病性视网膜病变，这种疾病如果治疗不当会导致患者视力出现不可逆转的损害。越早发现，患者不会失去视力的机会就越大。自动化手动程序的方法目前是有证据的，视网膜病变的诊断过程是手动的，医生在监视器上分析患者的视网膜。图像识别的实践可以通过识别糖尿病视网膜病变模式并将其与诊断中的患者视网膜进行比较来帮助这种检测。这种方法还可以辅助远程医疗的行为，其中无法参加考试的人可以从应用程序提供的诊断中受益。应用程序开发是通过卷积神经网络进行的，卷积神经网络对每个图像像素进行数字图像处理。使用 VGG-16 作为应用基础的预训练模型非常有用，最终模型准确率为 82%。