Improved CNN Model for Diabetic Retinopathy Analysis and Classification


  • Dipali R. Kasat, Swati B. Patil, Mohammed Marshall, Anushka Dambe


Deep learning,Diabetic Retinopathy,SVM,KNN,fundus images, CNNs, activation functions


Diabetes causes an increase in the amount of glucose in the blood due to a lack of insulin. Diabetes affects the retina, heart, nerves, and kidneys. Diabetic retinopathy is a significant complication. Mechanized methods for detecting diabetic retinopathy are more cost-effective and time-efficient than manual analysis. Deep Learning is an approach for computer-aided medical diagnosis. This research is an attempt to establish an automatic treatment for diabetic retinopathy in its early stages. Using Artificial Intelligence and Deep Learning, doctors can detect blindness before it occurs.In this study, we are utilizing a supervised learning strategy to classify fundus photos. For this task, we are using several image processing procedures and filters to improve many significant features such as microaneurysms, hemorrhages, exudates, and swollen blood vessels, all of which are features of fundus images that indicate that a person has Diabetic Retinopathy, and then using neural networks for classification.


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How to Cite

Dipali R. Kasat. (2024). Improved CNN Model for Diabetic Retinopathy Analysis and Classification. International Journal of Intelligent Systems and Applications in Engineering, 12(21s), 3933 –. Retrieved from



Research Article