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Authors: Mahsa Mahdinejad 1 ; Aidan Murphy 2 ; Patrick Healy 1 and Conor Ryan 1

Affiliations: 1 Univercity of Limerick, Limerick, Ireland ; 2 University Colledge Dublin, Dublin, Ireland

Keyword(s): Deep Learning, Evolutionary Algorithm, Image Segmentation.

Abstract: Deep learning is an excellent way for effectively addressing image processing, and several Neural Networks designs have been explored in this area. The Spatial Attention U-Net architecture, a version of the famous U-Net but which uses DropBlock and an attention block as well as the common U-Net convolutional blocks, is one notable example. Finding the best combination of hyper-parameters is expensive, time consuming and needs expert input. We show the genetic algorithm can be utilized to automatically determine the optimal combination of Spatial Attention U-Net hyper-parameters to train a model to solve a Retinal Blood Vessel Segmentation problem. Our new approach is able to find a model with an accuracy measure of 0.9855, an improvement from our previous experimentation which found a model with accuracy measure of 0.9751. Our new methods exhibit competitive performance with other state-of-the-art Retinal Blood Vessel Segmentation techniques.

CC BY-NC-ND 4.0

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Paper citation in several formats:
Mahdinejad, M.; Murphy, A.; Healy, P. and Ryan, C. (2022). Parameterising the SA-UNet using a Genetic Algorithm. In Proceedings of the 14th International Joint Conference on Computational Intelligence (IJCCI 2022) - ECTA; ISBN 978-989-758-611-8; ISSN 2184-3236, SciTePress, pages 97-104. DOI: 10.5220/0011528100003332

@conference{ecta22,
author={Mahsa Mahdinejad. and Aidan Murphy. and Patrick Healy. and Conor Ryan.},
title={Parameterising the SA-UNet using a Genetic Algorithm},
booktitle={Proceedings of the 14th International Joint Conference on Computational Intelligence (IJCCI 2022) - ECTA},
year={2022},
pages={97-104},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011528100003332},
isbn={978-989-758-611-8},
issn={2184-3236},
}

TY - CONF

JO - Proceedings of the 14th International Joint Conference on Computational Intelligence (IJCCI 2022) - ECTA
TI - Parameterising the SA-UNet using a Genetic Algorithm
SN - 978-989-758-611-8
IS - 2184-3236
AU - Mahdinejad, M.
AU - Murphy, A.
AU - Healy, P.
AU - Ryan, C.
PY - 2022
SP - 97
EP - 104
DO - 10.5220/0011528100003332
PB - SciTePress

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