| Title |
Res2U-Net: Double Resnet on U-Net for Exudate Segmentation in Retinal Image |
| DOI |
https://doi.org/10.5573/IEIESPC.2026.15.3.360 |
| Keywords |
Diabetes; Exudate; Health research; Health risk; Segmentation |
| Abstract |
The appearance of exudate on retina indicates diabetic retinopathy. An accurate segmentation method is needed to detect the presence of exudates, both hard exudates and soft exudates. U-shaped network (UNet) is a segmentation architecture. The varied forms of exudates require architecture with deep layers. However, adding layers to UNet can result in vanishing gradients during training. The study modifies the UNet architecture by replacing the encoder and decoder of UNet with residual blocks for exudate segmentation. The architecture is named a double residual block on a U-shaped network (RES2U-Net). The residual block allows gradients to flow directly across several layers without having to deal with non-linear operations. It can overcome vanishing gradients in UNet. The proposed architecture is expected to maintain the flow of important information and handle vanishing gradients in each layer so the exudate segmentation process in retinal images is optimal for both hard and soft exudates. The application of RES2U-Net to exudate segmentation produces an accuracy above 95%. The F1-Score results above 0.80 show that the proposed architecture has a good balance in separating exudate from unnecessary features. These results show that the proposed architecture can provide accurate and valid exudate segmentation results on retinal images. |