Retinal blood vessel segmentation using a deep learning method based on modified U-NET model-Reference-Cited by-同舟云学术

Retinal blood vessel segmentation using a deep learning method based on modified U-NET model

Published:2024-03-15 Issue: Volume: Page:
ISSN:1573-7721
Container-title:Multimedia Tools and Applications
language:en
Short-container-title:Multimed Tools Appl

Author:

Sanjeewani ,Yadav Arun Kumar,Akbar Mohd,Kumar Mohit,Yadav Divakar^ORCID

Publisher

Springer Science and Business Media LLC

Link

https://link.springer.com/content/pdf/10.1007/s11042-024-18696-w.pdf

Reference62 articles.

1. Aguirre-Ramos H, Avina-Cervantes JG, Cruz-Aceves I et al (2018) Blood vessel segmentation in retinal fundus images using Gabor filters, fractional derivatives, and expectation maximization. Appl Math Comput 339:568–587. https://doi.org/10.1016/j.amc.2018.07.057

2. Aguirre-Ramos H, Avina-Cervantes JG, Cruz-Aceves I et al (2018) Blood vessel segmentation in retinal fundus images using gabor filters, fractional derivatives, and expectation maximization. Appl Math Comput 339:568–587

3. Ahmed S, Srinivasu PN, Alhumam A et al (2022) Aal and internet of medical things for monitoring type-2 diabetic patients. Diagnostics 12(11). https://www.mdpi.com/2075-4418/12/11/2739

4. Atli İ, Gedik OS (2021) Sine-net: a fully convolutional deep learning architecture for retinal blood vessel segmentation. Eng Sci Technol Int J 24(2):271–283

5. Azzopardi G, Strisciuglio N, Vento M et al (2015) Trainable cosfire filters for vessel delineation with application to retinal images. Med Image Anal 19(1):46–57

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1. Enhanced Retinal Vessel Segmentation Using U-Net Framework;2024 IEEE International Conference on Contemporary Computing and Communications (InC4);2024-03-15