IDENTIFICATION OF RETINAL DISEASES USING ML

Retinal diseases such as diabetic retinopathy, glaucoma, and age-related macular degeneration are major causes of vision loss and blindness worldwide. Early detection is critical for effective treatment and prevention. Recent advances in deep learning have shown promise in medical image analysis. This study presents a novel approach using convolutional neural networks (CNNs) to detect retinal diseases from retina images. The CNN is trained on a large, labeled dataset of retina images and includes multiple convolutional, pooling, and fully connected layers for classification. Data augmentation techniques are used to enhance the dataset’s diversity and size. Our system effectively classifies retina images as normal or abnormal with high sensitivity and specificity, showing its potential for real-world applications. The motivation behind this work is the urgent need for accessible, early diagnosis of retinal diseases, especially in regions with limited access to ophthalmologists. Automated CNN-based analysis can significantly improve early detection rates, offering a scalable and cost-effective solution. This technology helps reduce preventable blindness and enhances quality of life by enabling timely and accurate screenings. Ultimately, it supports healthcare systems in managing retinal diseases more efficiently and equitably across different populations.