Diabetic Retinopathy Classification Using Deep Learning


  • Abbaraju Sai Sathwik Vellore Institute of Technology University image/svg+xml
  • Raghav Agarwal Vellore Institute of Technology University image/svg+xml
  • Ajith Jubilson E Vellore Institute of Technology University image/svg+xml
  • Santi Swarup Basa Maharaja Sriram Chandra Bhanja Deo University image/svg+xml




Diabetic retinopathy, DR, deep learning, automated system, fundus images


One of the main causes of adult blindness and a frequent consequence of diabetes is diabetic retinopathy (DR). To avoid visual loss, DR must be promptly identified and classified. In this article, we suggest an automated DR detection and classification method based on deep learning applied to fundus pictures. The suggested technique uses transfer learning for classification. On a dataset of 3,662 fundus images with real-world DR severity labels, we trained and validated our model. According to our findings, the suggested technique successfully detected and classified DR with an overall accuracy of 78.14%. Our model fared better than other recent cutting-edge techniques, illuminating the promise of deep learning-based strategies for DR detection and management. Our research indicates that the suggested technique may be employed as a screening tool for DR in a clinical environment, enabling early illness diagnosis and prompt treatment.


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How to Cite

Sathwik AS, Agarwal R, Jubilson E A, Basa SS. Diabetic Retinopathy Classification Using Deep Learning . EAI Endorsed Trans Perv Health Tech [Internet]. 2023 Nov. 8 [cited 2024 May 26];9. Available from: https://publications.eai.eu/index.php/phat/article/view/4335