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Authors: Vipul Popat 1 ; Mahsa Mahdinejad 2 ; 1 ; Oscar S. Dalmau Cedeño 3 ; Enrique Naredo 2 ; 1 and Conor Ryan 2 ; 1

Affiliations: 1 University of Limerick, Limerick, Ireland ; 2 Lero –Science Foundation Ireland Research Centre for Software, Ireland ; 3 Centro de Investigación en Matemáticas (CIMAT), Mexico

Keyword(s): Image Segmentation, U-Net, Deep Learning.

Abstract: Blood vessel extraction in digital retinal images is an important step in medical image analysis for abnormality detection and also obtaining good retinopathy diabetic diagnosis; this is often referred to as the Retinal Blood Vessel Segmentation task and current state-of-the-art approaches all use some form of neural networks. Designing neural network architecture and selecting appropriate hyper-parameters for a specific task is challenging. In recent works, increasingly more complex models are starting to appear, but in this work, we present a simple and small model with a very low number of parameters with good performance compared with the state of the art algorithms. In particular, we choose a standard Genetic Algorithm (GA) for selecting the parameters of the model and we use an expert-designed U-net based model, which has become a very popular tool in image segmentation problems. Experimental results show that GA is able to find a much shorter architecture and acceptable accura cy compared to the U-net manually designed. This finding puts on the right track to be able in the future to implement these models in portable applications. (More)

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Paper citation in several formats:
Popat, V.; Mahdinejad, M.; Cedeño, O.; Naredo, E. and Ryan, C. (2020). GA-based U-Net Architecture Optimization Applied to Retina Blood Vessel Segmentation. In Proceedings of the 12th International Joint Conference on Computational Intelligence (IJCCI 2020) - ECTA; ISBN 978-989-758-475-6; ISSN 2184-3236, SciTePress, pages 192-199. DOI: 10.5220/0010112201920199

@conference{ecta20,
author={Vipul Popat. and Mahsa Mahdinejad. and Oscar S. Dalmau Cedeño. and Enrique Naredo. and Conor Ryan.},
title={GA-based U-Net Architecture Optimization Applied to Retina Blood Vessel Segmentation},
booktitle={Proceedings of the 12th International Joint Conference on Computational Intelligence (IJCCI 2020) - ECTA},
year={2020},
pages={192-199},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010112201920199},
isbn={978-989-758-475-6},
issn={2184-3236},
}

TY - CONF

JO - Proceedings of the 12th International Joint Conference on Computational Intelligence (IJCCI 2020) - ECTA
TI - GA-based U-Net Architecture Optimization Applied to Retina Blood Vessel Segmentation
SN - 978-989-758-475-6
IS - 2184-3236
AU - Popat, V.
AU - Mahdinejad, M.
AU - Cedeño, O.
AU - Naredo, E.
AU - Ryan, C.
PY - 2020
SP - 192
EP - 199
DO - 10.5220/0010112201920199
PB - SciTePress

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