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This study presents a deep learning approach for the automated classification of retinal diseases using fundus images, focusing on conditions like diabetic retinopathy, cataracts, and glaucoma. The proposed model, which combines Artificial Neural Networks (ANN) with MobileNetV2 and DenseNet121 architectures, achieved a peak accuracy of 98.2% in identifying these diseases. By utilizing a balanced dataset and advanced feature extraction techniques, the research aims to enhance diagnostic efficiency and improv

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olevertallx

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EN