EFL-Net: An Efficient Lightweight Neural Network Architecture for Retinal Vessel Segmentation

Nasrin Akbari, Amirali Baniasadi

2023

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 parameters 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.

Paper Citation

in Harvard Style

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, SciTePress, pages 920-927. DOI: 10.5220/0011754700003417

in Bibtex Style

@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},
}

in EndNote Style

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
AU - Akbari N.
AU - Baniasadi A.
PY - 2023
SP - 920
EP - 927
DO - 10.5220/0011754700003417
PB - SciTePress