Diabetic retinopathy detection using machine learning

Retinal degeneration, encompassing disorders such as diabetic retinopathy, glaucoma, and age-related macular degeneration, is among the primary causes of visual impairment and blindness worldwide. Timely detection of these conditions is crucial for effective intervention and the prevention of irreversible vision loss. In recent years, deep learning approaches have shown significant potential in medical image analysis for accurate disease identification. This study presents an advanced technique for detecting retinal degeneration using Convolutional Neural Networks (CNNs) applied to retinal imaging data. The proposed CNN-based model processes retinal scans and classifies them as either normal or abnormal, drawing on a large dataset of labeled retinal images corresponding to various disease conditions. The network architecture consists of multiple convolutional and pooling layers, followed by fully connected layers to perform final classification. Additionally, data augmentation methods are employed to enhance dataset diversity and improve model robustness. Experimental evaluations demonstrate high sensitivity and specificity, underscoring the model’s effectiveness and suitability for real-world medical diagnostic applications.