Diabetic Retinopathy detection via Deep Learning

Untreated diabetic retinopathy (DR) can lead to blindness and visual impairment. Automated screening of DR through deep learning methods has shown promise. This study presents an automated diabetic retinopathy screening system based on deep learning algorithms. Convolutional neural net works (CNNs) are employed to analyze retinal images and detect
early signs of diabetic retinopathy. The algorithm is trained on a large dataset of annotated retinal images to learn complex patterns indicative of DR progression. Evaluation of the system demonstrates excellent sensitivity, specificity, and accuracy in detecting diabetic retinopathy, making it a valuable tool for efficient retinal screening.