DIABETIC RETINOPATHY DETECTION BY MEANS OF DEEP LEARNING
Keywords:
Diabetic Retinopathy, Deep Learning, InceptionV3, Fundus Images, CNN, Medical Image ClassificationAbstract
Diabetic Retinopathy (DR) is a leading cause of blindness among diabetic patients. DR occurs due to prolonged high blood sugar levels that damage the retinal blood vessels. Early detection of DR is vital for timely intervention and preventing vision loss. This paper proposes an automated DR detection and classification model using deep learning techniques, specifically the InceptionV3 architecture. A publicly available dataset containing over 35,000 retinal images is used for training and validation. The model achieves a test accuracy of 97.3% and is capable of identifying five stages of DR: No DR, Mild, Moderate, Severe, and Proliferative DR. A Flask-based web interface facilitates the user-friendly deployment of the model for clinical use.
