Diabetic Retinopathy Severity Detection using Convolutional Neural Network

• Main Authors: Shete Mayank, Sabnis Saahil, Rai Srijan, Birajdar Gajanan
• Format: Article
• Language: English
• Published: EDP Sciences 2020-01-01
• Series: ITM Web of Conferences
• Subjects: convolutional neural networks; hypertensive retinopathy; arteriosclerotic retinopathy; machine learning; transfer learning; eyepacs
• Online Access: https://www.itm-conferences.org/articles/itmconf/pdf/2020/02/itmconf_icacc2020_01012.pdf

Diabetic Retinopathy is one of the most prominent eye diseases and is the leading cause of blindness amongst adults. Automatic detection of Diabetic Retinopathy is important to prevent irreversible damage to the eye-sight. Existing feature learning methods have a lesser accuracy rate in computer aided diagnostics; this paper proposes a method to further increase the accuracy. Machine learning can be used effectively for the diagnosis of this disease. CNN and transfer learning are used for the severity classification and have achieved an accuracy of 73.9 percent. The use of XGBoost classifier yielded an accuracy of 76.5 percent.