DIABETIC RETINOPATHY DETECTION USING MACHINE LEARNING

Computer Engineering

Retinal degeneration, including conditions such as diabetic retinopathy, glaucoma, and age-related macular degeneration, represents one of the leading causes of visual impairment and blindness globally. Early identification of these disorders is essential for timely treatment and prevention of vision loss. In recent years, deep learning techniques have demonstrated remarkable success in analyzing medical images for disease diagnosis. This study introduces an advanced method for detecting retinal degeneration using Convolutional Neural Networks (CNN) applied to retinal images. The proposed CNN model analyzes retinal scans and classifies them into normal or abnormal categories. It is trained on a large dataset of labeled retinal images associated with specific disease conditions. The architecture incorporates multiple convolutional and pooling layers, followed by fully connected layers for final classification. To enhance performance, data augmentation techniques are utilized to improve dataset variability and robustness. Experimental results show that the proposed system achieves high sensitivity and specificity, highlighting its potential for practical implementation in medical diagnostics.

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