학술논문

Detection of diabetic Retinopathy using Retinal Fundus Images
Document Type
Conference
Source
2022 4th International Conference on Advances in Computing, Communication Control and Networking (ICAC3N) Advances in Computing, Communication Control and Networking (ICAC3N), 2022 4th International Conference on. :449-455 Dec, 2022
Subject
Communication, Networking and Broadcast Technologies
Components, Circuits, Devices and Systems
Computing and Processing
Engineered Materials, Dielectrics and Plasmas
Engineering Profession
Fields, Waves and Electromagnetics
General Topics for Engineers
Photonics and Electrooptics
Power, Energy and Industry Applications
Robotics and Control Systems
Signal Processing and Analysis
Transportation
Training
Deep learning
Diabetic retinopathy
Recurrent neural networks
Sensitivity
Blood vessels
Retina
Task analysis
Biomedical imaging
Image classification
Diabetic Retinopathy
Recurrent Neural Network Blood Vessels
Microaneurysms
Histogram Equalization
Pseudo-Colour
Grey Level Co-occurrence Matrix
Language
Abstract
Diabetic retinopathy is one of the diabetes consequences that affects the eyes. This is caused by damage to the blood vessels in the retina, the light-sensitive tissue in the rear of the eye. It may create no symptoms at first, or it may cause minor eyesight difficulties. When the blood vessels become damaged, they may leak and this leakage can cause dark spots on our vision. The DR can be detected by finding the Hard Exudate present in it. The deep networks are becoming deeper and more complex. So that adding more number of layers to a neural network can make it stronger for image related tasks. But the main disadvantage in adding more layers is that, it may greatly reduces the accuracy of the image and also the data models are complex. In order to overcome this drawback, Recurrent Neural Network can be introduced. The fundamental benefit of using a recurrent neural network is that it can represent a collection of data in such a way that each pattern may be presumed to be reliant on the one before it. It can process inputs of any length. Even if the input size is large, the model size will not change. It makes the training process faster and attains more accuracy while compared to other neural networks. This method greatly reduces the loss of accuracy because each layer knows the information of the top layers while updating the weights. This Recurrent Neural Network has more number of parameters , so it is obvious that it can produce better result as compared to other net.