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Hybrid UNet Architecture based on Residual Learning of Fundus Images for Retinal Vessel Segmentation
- Source :
- Journal of Physics: Conference Series. 2070:012104
- Publication Year :
- 2021
- Publisher :
- IOP Publishing, 2021.
-
Abstract
- This paper deals with the new segmentation techniques for retinal blood vessels on fundus images. This technique aims at extracting thin vessels to reduce the intensity difference between thick and thin vessels. This paper proposes the modified UNet model by incorporating ResNet blocks into it which includes structured prediction. In this work we generate the visualization of blood vessels from retinal fundus image for two loss functions namely cross entropy loss and Dice loss where the network classifies several pixels simultaneously. The results shows higher accuracy by considering a much more expressive UNet algorithm and outperforms the past algorithms for Retinal Vessel Segmentation. The benefits of this approach will be demonstrated empirically.
Details
- ISSN :
- 17426596 and 17426588
- Volume :
- 2070
- Database :
- OpenAIRE
- Journal :
- Journal of Physics: Conference Series
- Accession number :
- edsair.doi...........197f7c46f9002fe8c2d20f28856c1e80