Diabetic retinopathy detection using machine learning

Retinal degeneration, which includes conditions such as diabetic retinopathy, glaucoma, and age-related macular degeneration, is one of the leading causes of vision impairment and blindness globally. Early detection plays a vital role in enabling timely treatment and preventing permanent vision loss. In recent years, deep learning techniques have demonstrated strong capabilities in medical image analysis, particularly for accurate disease detection. This study introduces an advanced method for identifying retinal degeneration using Convolutional Neural Networks (CNNs) applied to retinal images. The proposed CNN model analyses retinal scans and classifies them into normal or abnormal categories using a large, labelled dataset representing various retinal diseases. The architecture incorporates multiple convolutional and pooling layers, followed by fully connected layers for final classification. To further improve performance, data augmentation techniques are utilized to increase dataset diversity and enhance model robustness. Experimental results show high sensitivity and specificity, highlighting the model’s effectiveness and its potential for real-world clinical applications.