DRS-UNET: A Deep Learning Approach for Diabetic Retinopathy Detection and Segmentation from Fundus Images

Abstract

Diabetic Retinopathy (DR) is the main cause of blindness in working-age adults around the world. Early detection and treatment of DR are critical for preventing vision loss. Image segmentation is a critical step in automated DR detection. UNET is a well-known convolutional neural network design for image segmentation. The typical UNET architecture, on the other hand, may not necessarily be appropriate for DR detection. This study introduces DRS UNET, an unique architecture specifically built for DR detection. DRS UNET incorporates residual blocks and attention mechanisms to improve feature extraction and segmentation performance. The proposed model is trained and tested using publically available datasets, yielding cutting-edge results.