Segmentation of Retinal Blood Vessels Using Deep Learning
arXiv:2303.09679v13 citationsh-index: 30
Originality Synthesis-oriented
AI Analysis
This work addresses the problem of automating retinal image analysis for disease diagnosis, but it is incremental as it compares existing methods.
This project compared four neural network architectures for segmenting retinal blood vessels in images, finding that UNet-ResNet achieved the best performance with a Dice coefficient of 0.85.
The morphology of retinal blood vessels can indicate various diseases in the human body, and researchers have been working on automatic scanning and segmentation of retinal images to aid diagnosis. This project compares the performance of four neural network architectures in segmenting retinal images, using a combined dataset from different databases, namely the UNet, DR-VNet, UNet-ResNet and UNet-VGG.