EYE DISEASE PREDICTION USING IMAGE PROCESSING AND DEEP LEARNING

The prevalence of eye diseases worldwide underscores the importance of early detection and timely intervention to prevent vision impairment and blindness. In this paper, we propose an innovative approach for Eye Disease Prediction using Image Processing and Deep Learning techniques. Leveraging advancements in medical imaging and machine learning, our system analyzes retinal fundus images to identify signs of various eye diseases, including diabetic retinopathy, glaucoma, and age-related macular degeneration. The proposed system comprises several key components, including image preprocessing, feature extraction, and classification using deep neural networks. Preprocessing techniques are applied to enhance image quality and remove noise, while feature extraction algorithms extract relevant features from retinal images. Deep learning models, such as convolutional neural networks (CNNs), are then trained on the extracted features to classify retinal images into different disease categories. Preliminary evaluation results demonstrate the effectiveness of our approach in accurately predicting eye diseases from retinal images. The system achieves high classification accuracy and sensitivity, outperforming traditional diagnostic methods and reducing the need for manual interpretation by ophthalmologists. Furthermore, the system’s scalability and potential for integration with existing healthcare systems make it a valuable tool for population-based screening and early disease detection initiatives. Overall, the proposed Eye Disease Prediction system represents a significant advancement in leveraging image processing and deep learning technologies for proactive eye healthcare. By enabling early detection and intervention, the system has the potential to improve patient outcomes, reduce healthcare costs, and alleviate the burden of eye diseases on healthcare systems worldwide.