Automated Classification of Retinal Diseases

Retinal diseases like diabetic retinopathy, age-related macular degeneration, and glaucoma are major causes of vision impairment worldwide. Early diagnosis is vital but often hindered by limited access to specialized care, particularly in low-resource settings. This study introduces an automated system for retinal disease classification using Convolutional Neural Networks (CNNs) to analyze retinal fundus images. The system incorporates preprocessing techniques to address variability in image quality and noise, achieving high accuracy in classifying common retinal conditions. Designed with a user-friendly interface, it enables clinicians to upload images and receive real-time diagnostic results, enhancing decision-making efficiency. Developed in Python with Jupyter Notebook, the system demonstrates the potential of deep learning in delivering cost-effective and scalable diagnostic tools. Future work will expand datasets, improve model generalizability, and integrate the system into telemedicine platforms to improve accessibility. Keywords-Retinal diseases, Automated diagnosis, Convolutional Neural Networks (CNNs), Retinal fundus images, Real-time diagnostics, Artificial intelligence in healthcare