• Akshay Chobhe
• Ashish Bagate
• Indraneel Sarode
• Shubham Shewale
• Prof. Dipalee Rane

Diabetic retinopathy, a cause of vision loss requires early and accurate detection. This study introduces a method, for detecting retinopathy using machine learning techniques. We utilize Support Vector Machines (SVM) Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN) to ensure classification. The system analyzes features from high resolution fundus images to enable grading. By combining SVM, CNN and RNN strengths our approach outperforms methods, offering a path for improved diabetic retinopathy diagnosis. Our models experimental results demonstrate accuracy and consistency in identifying stages of DR(Diabetic Retinopathy). This research contributes to advancing automated diagnosis with the goal of impacting the timely treatment of diabetic retinopathy and preserving vision. Experiments on the Messidor dataset confirm the effectiveness of our proposed model, in grading DR.

Diabetic Retinopathy, Machine Learning, Support Vector Machines, Convolutional Neural Networks, Recurrent Neural Networks, Disease Classification, Integrated Approach.

"Advancements In Machine Learning Techniques For Early Diagnosis Of Diabetic Retinopathy", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 5, page no.p197-p201, May-2024, Available :http://www.jetir.org/papers/JETIR2405G29.pdf

"Advancements In Machine Learning Techniques For Early Diagnosis Of Diabetic Retinopathy", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 5, page no. ppp197-p201, May-2024, Available at : http://www.jetir.org/papers/JETIR2405G29.pdf