Multi-disease Retinal Vessel Segmentation: A Deep Learning Approach

Mohammad Tariqul Islam, Hesham Zaky, Tanvir Alam*

*Corresponding author for this work

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

Abstract

The structure of blood vessels in the retina is a crucial factor in identifying and forecasting various eye diseases like cardiovascular diseases, diabetes, and other diseases. Therefore, detecting the structure of blood vessels from retinal fundus images is a critical field of research in healthcare. This study employed a novel deep learning model to segment vessels for different diseases, including Glaucoma, Diabetic Retinopathy (DR), and Age-related Macular Degeneration (AMD). We considered multiple transfer learning-based models and discovered that the ResNet-based U-Net architecture was the most effective for vessel segmentation, achieving the highest Dice Score above 84% for disease-agnostic, and 82%-84% for disease-specific conditions. We believe the proposed methodology will help to advance retinal vessel segmentation process and enhance the screening process of diseases based on retinal fundus images in clinical settings of Qatar Biobank as well as other biobanks across the globe.

Original languageEnglish
Title of host publication2023 6th International Conference on Signal Processing and Information Security, ICSPIS 2023
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages139-144
Number of pages6
ISBN (Electronic)9798350329599
DOIs
Publication statusPublished - 2023
Event6th International Conference on Signal Processing and Information Security, ICSPIS 2023 - Dubai, United Arab Emirates
Duration: 8 Nov 20239 Nov 2023

Publication series

Name2023 6th International Conference on Signal Processing and Information Security, ICSPIS 2023

Conference

Conference6th International Conference on Signal Processing and Information Security, ICSPIS 2023
Country/TerritoryUnited Arab Emirates
CityDubai
Period8/11/239/11/23

Keywords

  • Diabetic Retinopathy
  • Glaucoma
  • Qatar Biobank
  • Retina
  • Segmentation

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