Diabetic Retinopathy using CNN - Alex net

Diabetic retinopathy (DR) is a severe complication of diabetes that can lead to vision impairment and blindness if not detected and treated early. This study explores the efficacy of Convolutional Neural Network (CNN) architecture, specifically the AlexNet model, in automating the detection of diabetic retinopathy from retinal images. Leveraging the power of deep learning, the proposed model is trained on a comprehensive dataset of diverse retinal images, encompassing various stages of DR progression. The AlexNet architecture, renowned for its success in image classification tasks, is fine-tuned to discern subtle patterns and anomalies indicative of diabetic retinopathy. The model's performance is rigorously evaluated using established metrics, including sensitivity, specificity, and area under the Receiver Operating Characteristic curve. Our findings demonstrate the CNN-AlexNet’s superior ability to accurately identify diabetic retinopathy, showcasing its potential as a valuable tool for early diagnosis and intervention. The integration of deep learning technologies in diabetic retinopathy screening holds promise for enhancing healthcare outcomes and reducing the burden on healthcare systems.

Key Words: diabetic retinopathy; image classification; deep convolutional neural network, CNN-AlexNet’s.