DEEPDIABETIC: A DEEP NEURAL NETWORK FRAMEWORK FOR AUTOMATED DETECTION OF DIABETIC EYE DISEASES

Abstract

Diabetic Retinopathy and other diabetic eye diseases are major causes of vision impairment and blindness among diabetic patients worldwide. Early detection and timely treatment are essential to prevent severe vision loss, but traditional manual diagnosis by ophthalmologists is time-consuming and requires expert analysis of retinal images. The proposed DEEPDIABETIC framework utilizes Artificial Intelligence and Deep Learning techniques to automatically detect diabetic eye diseases from retinal fundus images with high accuracy and efficiency. The system employs deep neural network architectures for image preprocessing, feature extraction, and disease classification to identify abnormalities such as microaneurysms, hemorrhages, and retinal lesions. The automated framework improves diagnostic speed, reduces human error, and supports early disease detection in real-time clinical environments. By providing accurate and intelligent screening assistance, the proposed system enhances healthcare accessibility, supports ophthalmologists in medical decision-making, and helps prevent vision-related complications among diabetic patients.