Hybrid Genetic U-Net Algorithm for Medical Segmentation

Jon-Olav Holland, Youcef Djenouri, Roufaida Laidi, Anis Yazidi

2023

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.

Paper Citation

in Harvard Style

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, pages 558-564. DOI: 10.5220/0011703700003393

in Bibtex Style

@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},
}

in EndNote Style

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
AU - Holland J.
AU - Djenouri Y.
AU - Laidi R.
AU - Yazidi A.
PY - 2023
SP - 558
EP - 564
DO - 10.5220/0011703700003393