Diabetic Retinopathy Detection by means of Deep Learning
- Resource Type
- Conference
- Authors
- Thorat, Sumit; Chavan, Akshay; Sawant, Pratik; Kulkarni, Sharvika; Sisodiya, Nitin; Kolapkar, Anand
- Source
- 2021 5th International Conference on Intelligent Computing and Control Systems (ICICCS) Intelligent Computing and Control Systems (ICICCS), 2021 5th International Conference on. :996-999 May, 2021
- Subject
- Communication, Networking and Broadcast Technologies
Components, Circuits, Devices and Systems
Computing and Processing
Robotics and Control Systems
Signal Processing and Analysis
Deep learning
Measurement
Retinopathy
Neural networks
Graphics processing units
Manuals
Medical services
Convolutional Neural Network
Retinal Images
Deep Learning
Classification
Dropout
Max Pooling
- Language
Diabetic Retinopathy (DR) is a complication caused by diabetes that affects the human eye. It is caused by the mutilation of the blood vessels of the light-sensitive tissue at the back of the human retina. It's the most recurrent cause of blindness in the working-age group of people and is highly likely when diabetes is poorly controlled. Although, methods to detect Diabetic Retinopathy exist, they involve manual examination of the retinal image by an Ophthalmologist. The Proposed approach of DR detection aims to detect the complication in an automated manner using Deep Learning. The model is trained using a GPU on 35126 retinal images released publicly by eyePACS on the Kaggle website and achieved an accuracy of approximately 81%.