论文标题
通过视网膜图像识别的糖尿病性视网膜病变检测
Diabetic Retinopathy detection by retinal image recognizing
论文作者
论文摘要
许多人受到世界各地糖尿病的影响。该疾病可能患有1型和2型。糖尿病带来了几种并发症，包括糖尿病性视网膜病，这种并发症是一种疾病，如果无法正确治疗，可能会导致患者视力中的不可逆损害。检测到越早，患者不会失去视力的机会就越好。目前有自动化手动程序的方法，视网膜病变的诊断过程是手动的，医生在监测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性视网膜病变模式并将其与患者的视网膜进行诊断时进行帮助。这种方法还可以帮助远程医疗行为，在这种情况下，无法参加考试的人们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，卷积神经网络对分析每个图像像素进行数字图像处理。将VGG-16用作预先培训的模型在应用程序基础上非常有用，最终模型精度为82％。
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%.
