• Nelson Kingsley Joel P
• Karthick S
• Krishna Moorthy S
• Sanjay M
• Ponnudurai J

Prediction of diabetic retinopathy on early stage is an interesting task in medical image diagnosis system. In this approach, we proposed a novel Efficient Retinopathy Prediction Algorithm (ERPA) for efficiently classify and predict the various stages of retinopathy. We proposed an algorithm, which combines the Convolutional Neural Network (CNN) and Support vector machine (SVM) to predict the retinopathy in digital image. The novelty of the work is that we use the CNN for feature extraction and SVM for classification to predict with more accuracy. The analysis results shows that the proposed ERPA provides the high results compare than various learning-based methods. The experimental results are verified with the google colab tool and the Kaggle dataset is tested.

Retinopathy Prediction, Convolutional Neural Network, Support Vector Machine, Machine Learning, Digital Image Processing

"An Analysis of Diabetic Retinopathy Prediction Using Efficient Retinopathy Prediction Algorithm", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 5, page no.c337-c341, May-2024, Available :http://www.jetir.org/papers/JETIR2405240.pdf

"An Analysis of Diabetic Retinopathy Prediction Using Efficient Retinopathy Prediction Algorithm", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 5, page no. ppc337-c341, May-2024, Available at : http://www.jetir.org/papers/JETIR2405240.pdf