A Study on Designing a Classifier System for Diagnosing Diabetic Retinopathy in Retinal Images Using AI: CNN and RNN Approaches






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A Study on Designing a Classifier System for Diagnosing Diabetic Retinopathy in Retinal Images Using AI: CNN and RNN Approaches

N Prathyusha,

Dr Pratik R Hajare, Professor

Manasarovar Global University

Abstract
Diabetic Retinopathy (DR) is one of the major causes of vision impairment globally. Early detection is crucial to avoid irreversible damage. This paper presents a deep learning-based classifier system that utilizes Convolutional Neural Networks (CNN) for feature extraction and Recurrent Neural Networks (RNN), particularly LSTM, for classification. The system is evaluated on public datasets such as EyePACS and Messidor. The hybrid model achieves improved accuracy compared to standalone CNN or RNN architectures, demonstrating the effectiveness of combining spatial and sequential learning for DR diagnosis.

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A Study on Designing a Classifier System for Diagnosing Diabetic Retinopathy in Retinal Images Using AI: CNN and RNN Approaches

A Study on Designing a Classifier System for Diagnosing Diabetic Retinopathy in Retinal Images Using AI: CNN and RNN Approaches

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