loading
Papers Papers/2022 Papers Papers/2022

Research.Publish.Connect.

Paper

Authors: Nasrin Akbari and Amirali Baniasadi

Affiliation: Department of Electrical and Computer Engineering, University of Victoria, Victoria, Canada

Keyword(s): Blood Vessel Segmentation, Deep Learning, Image Processing.

Abstract: Accurate segmentation of retinal vessels is crucial for the timely diagnosis and treatment of conditions like diabetes and hypertension, which can prevent blindness. Deep learning algorithms have been successful in segmenting retinal vessels, but they often require a large number of parameters and computations. To address this, we propose an efficient and fast lightweight network (EFL-Net) for retinal blood vessel segmentation. EFL-Net includes the ResNet branches shuffle block (RBS block) and the Dilated Separable Down block (DSD block) to extract features at various granularities and enhance the network receptive field, respectively. These blocks are lightweight and can be easily integrated into existing CNN models. The model also uses PixelShuffle as an upsampling layer in the decoder, which has a higher capacity for learning features than deconvolution and interpolation approaches. The model was tested on the Drive and CHASEDB1 datasets and achieved excellent results with fewer p arameters compared to other networks such as ladder net and DCU-Net. EFL-Net achieved F1 measures of 0.8351 and 0.8242 on the CHASEDB1 and DRIVE datasets, respectively, with 0.340 million parameters, compared to 1.5 million for ladder net and 1 million for DCU-Net. (More)

CC BY-NC-ND 4.0

Sign In Guest: Register as new SciTePress user now for free.

Sign In SciTePress user: please login.

PDF ImageMy Papers

You are not signed in, therefore limits apply to your IP address 3.15.46.221

In the current month:
Recent papers: 100 available of 100 total
2+ years older papers: 200 available of 200 total

Paper citation in several formats:
Akbari, N. and Baniasadi, A. (2023). EFL-Net: An Efficient Lightweight Neural Network Architecture for Retinal Vessel Segmentation. In Proceedings of the 18th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2023) - Volume 4: VISAPP; ISBN 978-989-758-634-7; ISSN 2184-4321, SciTePress, pages 920-927. DOI: 10.5220/0011754700003417

@conference{visapp23,
author={Nasrin Akbari. and Amirali Baniasadi.},
title={EFL-Net: An Efficient Lightweight Neural Network Architecture for Retinal Vessel Segmentation},
booktitle={Proceedings of the 18th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2023) - Volume 4: VISAPP},
year={2023},
pages={920-927},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011754700003417},
isbn={978-989-758-634-7},
issn={2184-4321},
}

TY - CONF

JO - Proceedings of the 18th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2023) - Volume 4: VISAPP
TI - EFL-Net: An Efficient Lightweight Neural Network Architecture for Retinal Vessel Segmentation
SN - 978-989-758-634-7
IS - 2184-4321
AU - Akbari, N.
AU - Baniasadi, A.
PY - 2023
SP - 920
EP - 927
DO - 10.5220/0011754700003417
PB - SciTePress

- Science and Technology Publications, Lda.
RESOURCES

Proceedings

Papers

Authors

Ontology

CONTACTS

Science and Technology Publications, Lda
Avenida de S. Francisco Xavier, Lote 7 Cv. C,
2900-616 Setúbal, Portugal.

Phone: +351 265 520 185 (National fixed network call)
Fax: +351 265 520 186
Email: info@scitepress.org

EXTERNAL LINKS

PRIMORIS

INSTICC

SCITEVENTS

CROSSREF

PROCEEDINGS SUBMITTED FOR INDEXATION BY:

dblp

Ei Compendex

SCOPUS

Semantic Scholar

Google Scholar

Microsoft Academic