loading
Papers Papers/2022 Papers Papers/2022

Research.Publish.Connect.

Paper

Authors: Jon-Olav Holland 1 ; Youcef Djenouri 2 ; Roufaida Laidi 3 and Anis Yazidi 1

Affiliations: 1 OsloMet, Oslo, Norway ; 2 NORCE Norwegian Research Centre, Oslo, Norway ; 3 CERIST, Algiers, Algeria

Keyword(s): Hyperparameter Optimization, Genetic Algorithm, U-Net, Medical Applications.

Abstract: U-Net based architecture has become the de-facto standard approach for medical image segmentation in recent years. Many researchers have used the original U-Net as a skeleton for suggesting more advanced models such as UNet++ and UNet 3+. This paper seeks to boost the performance of the original U-Net via optimizing its hyperparameters. Rather than changing the architecture itself, we optimize hyperparameters which does not affect the architecture, but affects the performance of the model. For this purpose, we use genetic algorithms. Intensive experiments on medical dataset have been carried out which document a performance gain at a low computation cost. In addition, preliminary results reveal the benefit of the proposed framework for medical image segmentation.

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 3.137.187.233

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:
Holland, J.; Djenouri, Y.; Laidi, R. and Yazidi, A. (2023). Hybrid Genetic U-Net Algorithm for Medical Segmentation. In Proceedings of the 15th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART; ISBN 978-989-758-623-1; ISSN 2184-433X, SciTePress, pages 558-564. DOI: 10.5220/0011703700003393

@conference{icaart23,
author={Jon{-}Olav Holland. and Youcef Djenouri. and Roufaida Laidi. and Anis Yazidi.},
title={Hybrid Genetic U-Net Algorithm for Medical Segmentation},
booktitle={Proceedings of the 15th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART},
year={2023},
pages={558-564},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011703700003393},
isbn={978-989-758-623-1},
issn={2184-433X},
}

TY - CONF

JO - Proceedings of the 15th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART
TI - Hybrid Genetic U-Net Algorithm for Medical Segmentation
SN - 978-989-758-623-1
IS - 2184-433X
AU - Holland, J.
AU - Djenouri, Y.
AU - Laidi, R.
AU - Yazidi, A.
PY - 2023
SP - 558
EP - 564
DO - 10.5220/0011703700003393
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