Skin Lesion Segmentation Using Attention-Based DenseUNet

Anwar Jimi, Hind Abouche, Nabila Zrira, Ibtissam Benmiloud

2023

Abstract

Skin lesion segmentation in dermoscopic images is still a challenging problem due to the blurry borders and low contrast of the lesions. Deep learning networks, like U-Net, have been successfully used to segment medical images over the past few years, and their performance has improved in terms of time and accuracy. This paper proposes an automated method for segmenting lesion boundaries that combines two architectures (i.e., the U-Net and the DenseNet as backbone) as well as the attention mechanism. Moreover, we also used adaptive gamma correction to enhance the contrast of the image, which considerably enhanced the segmentation results. Furthermore, we trained our model on the ISIC 2016, the ISIC 2017, and the ISIC 2018 datasets. Finally, the qualitative and quantitative experimental results of the skin lesion segmentation are very promising.

Paper Citation

in Harvard Style

Jimi A., Abouche H., Zrira N. and Benmiloud I. (2023). Skin Lesion Segmentation Using Attention-Based DenseUNet. In Proceedings of the 16th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2023) - Volume 3: BIOINFORMATICS; ISBN 978-989-758-631-6, SciTePress, pages 91-100. DOI: 10.5220/0011686400003414

in Bibtex Style

@conference{bioinformatics23,
author={Anwar Jimi and Hind Abouche and Nabila Zrira and Ibtissam Benmiloud},
title={Skin Lesion Segmentation Using Attention-Based DenseUNet},
booktitle={Proceedings of the 16th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2023) - Volume 3: BIOINFORMATICS},
year={2023},
pages={91-100},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011686400003414},
isbn={978-989-758-631-6},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 16th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2023) - Volume 3: BIOINFORMATICS
TI - Skin Lesion Segmentation Using Attention-Based DenseUNet
SN - 978-989-758-631-6
AU - Jimi A.
AU - Abouche H.
AU - Zrira N.
AU - Benmiloud I.
PY - 2023
SP - 91
EP - 100
DO - 10.5220/0011686400003414
PB - SciTePress