TY - GEN
T1 - Multi-disease Retinal Vessel Segmentation
T2 - 6th International Conference on Signal Processing and Information Security, ICSPIS 2023
AU - Islam, Mohammad Tariqul
AU - Zaky, Hesham
AU - Alam, Tanvir
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - 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.
AB - 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.
KW - Diabetic Retinopathy
KW - Glaucoma
KW - Qatar Biobank
KW - Retina
KW - Segmentation
UR - http://www.scopus.com/inward/record.url?scp=85182269990&partnerID=8YFLogxK
U2 - 10.1109/ICSPIS60075.2023.10343981
DO - 10.1109/ICSPIS60075.2023.10343981
M3 - Conference contribution
AN - SCOPUS:85182269990
T3 - 2023 6th International Conference on Signal Processing and Information Security, ICSPIS 2023
SP - 139
EP - 144
BT - 2023 6th International Conference on Signal Processing and Information Security, ICSPIS 2023
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 8 November 2023 through 9 November 2023
ER -