Importance of Data Augmentation and Transfer Learning on Retinal Vessel Segmentation

Sabri Deari, Ilkay Oksuz, Sezer Ulukaya

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

2 Citations (Scopus)


Automatic segmentation of retinal fundus images for extracting blood vessels is an essential task in the diagnostic classification of hypertension, glaucoma, and diabetic retinopathy, which are the leading causes of blindness. In this paper, we employed a transfer learning strategy for improved retinal vessel extraction. Firstly, we trained the U-NET model on CHASE DB1 and DRIVE databases. By using data augmentations on datasets we enable the U-NET model to learn retinal vessel features better. We examined the data augmentation types, namely, pixel-level transformations and affine transformations. Secondly, we utilized the transfer learning approach on two datasets and achieved comparable results with the state-of-the-art studies on retinal vessel segmentation task. Also, we employed combination of affine and pixel-level transformations to further boost segmentation performance.

Original languageEnglish
Title of host publication2021 29th Telecommunications Forum, TELFOR 2021 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781665425841
Publication statusPublished - 2021
Event29th Telecommunications Forum, TELFOR 2021 - Virtual, Belgrade, Serbia
Duration: 23 Nov 202124 Nov 2021

Publication series

Name2021 29th Telecommunications Forum, TELFOR 2021 - Proceedings


Conference29th Telecommunications Forum, TELFOR 2021
CityVirtual, Belgrade

Bibliographical note

Publisher Copyright:
© 2021 IEEE.


  • Data augmentation
  • Retinal blood vessel
  • Segmentation
  • Transfer learning
  • U-NET


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