Advanced hybrid UNet architectures for retinal vessel segmentation

Retinal blood vessel segmentation is a critical step in the early detection and diagnosis of various vision-threatening diseases, including diabetic retinopathy, hypertension, and glaucoma. Manual segmentation by medical professionals is time-consuming, subjective, and prone to variability, highlighting the need for robust automated solutions. Recent advancements in deep learning have shown significant promise in addressing these challenges by enabling accurate and efficient segmentation of retinal blood vessels from fundus images. In this paper, we propose three advanced deep learning architectures for retinal blood vessel segmentation: VGG16-UNet, VGG19-UNet, and ResNet50-UNet. These models combine the strengths of pre-trained convolutional neural networks (CNNs) with the U-Net architecture, leveraging transfer learning to enhance feature extraction and segmentation performance. We evaluate the proposed models on a publicly available retinal image dataset, achieving Dice coefficients of 79%, 79% and 80% for VGG16-UNet, VGG19-UNet, and ResNet50-UNet, respectively. Our results demonstrate that the ResNet50-UNet model outperforms the other two variants and surpasses several state-of-the-art methods in terms of segmentation accuracy and robustness. This study underscores the potential of deep learning-based approaches for improving the automation and reliability of retinal blood vessel segmentation, paving the way for more efficient and accurate diagnostic tools in ophthalmology.