Diabetic Retinopathy detection by retinal image recognizing

Abstract

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%.

10 source codes, 12 figures, 48 pages

References in corpus (7)

• Improving neural networks by preventing co-adaptation of feature detectors
• How transferable are features in deep neural networks?
• Smart Augmentation - Learning an Optimal Data Augmentation Strategy
• Advancements in Image Classification using Convolutional Neural Network
• Transfer learning for time series classification
• A Review of Object Detection Models based on Convolutional Neural Network
• Early stopping and non-parametric regression: An optimal data-dependent stopping rule