A Streamlined Lesion Segmentation Method Using Deep Learning and Image Processing for a Further Melanoma Diagnosis

Jinen Daghrir, Wafa Mbarki, Lotfi Tlig, Moez Bouchouicha, Noureddine Litaiem, Faten Zeglaoui, Mounir Sayadi

2025

Abstract

Over the past two decades, the world has known a significant number of deaths from cancer. More specifically, melanoma which is considered as the deadliest form of skin cancer causes a remarkable percentage of all cancer deaths. Therefore, the health and disease management community has exceedingly invested in creating automated systems to help doctors better analyze such diseases. Correspondingly, we were interested in creating an automatic lesion detection task for further melanoma diagnosis. The lesion segmentation is considered to be a critical step in a pattern recognition system. Our proposed segmentation technique consists of finding lesions’ masks using a baseline, edge-based, and more sophisticated and state-of-the-art method: thresholding using Otsu’s technique, morphological snakes, and a fully CNN (Convolutional Neural Network) model based on the U-net architecture, respectively. These methods are commonly used when dealing with skin lesion segmentation, and each one of them has its advantages and drawbacks. The U-net architecture is improved by the use of the pre-trained encoder ResNet-50 on the ImageNet dataset. A majority voting is applied to generate the final segmentation map using these three methods. The experiments were conducted using a benchmark dataset and showed promising results compared to using these methods separately, the majority voting of the three methods can significantly improve the segmentation task by refining the borders of the masks issued by the Deep learning model.

Paper Citation

in Harvard Style

Daghrir J., Mbarki W., Tlig L., Bouchouicha M., Litaiem N., Zeglaoui F. and Sayadi M. (2025). A Streamlined Lesion Segmentation Method Using Deep Learning and Image Processing for a Further Melanoma Diagnosis. In Proceedings of the 11th International Conference on Information and Communication Technologies for Ageing Well and e-Health - Volume 1: ICT4AWE; ISBN 978-989-758-743-6, SciTePress, pages 368-376. DOI: 10.5220/0013472400003938

in Bibtex Style

@conference{ict4awe25,
author={Jinen Daghrir and Wafa Mbarki and Lotfi Tlig and Moez Bouchouicha and Noureddine Litaiem and Faten Zeglaoui and Mounir Sayadi},
title={A Streamlined Lesion Segmentation Method Using Deep Learning and Image Processing for a Further Melanoma Diagnosis},
booktitle={Proceedings of the 11th International Conference on Information and Communication Technologies for Ageing Well and e-Health - Volume 1: ICT4AWE},
year={2025},
pages={368-376},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013472400003938},
isbn={978-989-758-743-6},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 11th International Conference on Information and Communication Technologies for Ageing Well and e-Health - Volume 1: ICT4AWE
TI - A Streamlined Lesion Segmentation Method Using Deep Learning and Image Processing for a Further Melanoma Diagnosis
SN - 978-989-758-743-6
AU - Daghrir J.
AU - Mbarki W.
AU - Tlig L.
AU - Bouchouicha M.
AU - Litaiem N.
AU - Zeglaoui F.
AU - Sayadi M.
PY - 2025
SP - 368
EP - 376
DO - 10.5220/0013472400003938
PB - SciTePress