Diabetic Retinopathy Detection Through Retinal Vessel Segmentation Using Swin U-Net Model

Diabetic Retinopathy (DR) is a common ocular illness that affects people in their working years and can cause vision loss. Blood vessel segmentation is crucial for the early detection and management of DR. It helps identify early warning signs including hemorrhages and microaneurysms, allowing for prompt intervention to prevent vision loss. In addition, it provides quantitative measures such as tortuosity and vascular density, which aid in making treatment decisions and charting the progression of the disease over time. But artificial intelligence (AI), and in particular deep learning (DL), is about to change medicine gradually and affect almost every area of human life. Significant improvements in diagnostic technologies have made it easier to gain insight into retinal conditions. Auto-mated screening methods considerably expedite DR detection, especially in resource-constrained contexts. Automatic blood vessel segmentation significantly improves patient outcomes in diabetic retinopathy screening programs by facilitating timely intervention and precise treatment recommendations. In this work, we apply a segmentation method to the color fundus images. First, we started by preprocessing using the CLAHE method, data augmentation, and resizing. Swin-UNet was then applied to segment retinal vessels on the RITE, Drive, STARE, and ChaseDB1 datasets. The used Swin U-Net model obtained a specificity of 92.83%, a sensitivity of 94.67%, and a Dice score of 91.24% on the ChaseDB1, STARE, and Drive datasets. Our model obtained a specificity of 86%, a sensitivity of 85%, and a Dice score of 87% on the RITE dataset.