A Real-World Segmentation Model for Melanocytic and Nonmelanocytic Dermoscopic Images

Eleonora Melissa, Daria Riabitch, Linda Lazzeri, Federica La Rosa, Chiara Benvenuti, Mario D’Acunto, Giovanni Bagnoni, Daniela Massi, Marco Laurino

2025

Abstract

Segmentation is a critical step in computer-aided diagnosis (CAD) systems for skin lesion classification. In this study, we applied the Deeplabv3+ network to segment real dermoscopic images. The model was trained on public datasets and tested both on public and on a disjoint set of images from the TELEMO project, covering six clinically significant skin lesion types: basal cell carcinoma, squamous cell carcinoma, melanoma, benign nevi, actinic keratosis and seborrheic keratosis. Our model achieved a testing global accuracy of 97.88% on public dataset and of 92.62% on TELEMO dataset, outperforming literature models. Although some misclassifications occurred, largely due to class imbalance, the model demonstrated strong generalization to real-world clinical images, a critical achievement for deep learning in medical imaging. To evaluate the clinical relevance of our segmentation, we extracted ten key features related to lesion border and diameter. Notably, the ”Diameters Mean” and ”Area to Perimeter Product” features revealed significant differences between melanoma-nevi and basal cell carcinoma-nevi classes, with strong effect sizes. These findings suggest that border features are crucial for distinguishing between multiple skin lesion types, highlighting the model’s potential for aiding dermatological diagnoses.

Paper Citation

in Harvard Style

Melissa E., Riabitch D., Lazzeri L., La Rosa F., Benvenuti C., D’Acunto M., Bagnoni G., Massi D. and Laurino M. (2025). A Real-World Segmentation Model for Melanocytic and Nonmelanocytic Dermoscopic Images. In Proceedings of the 18th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 1: BIOIMAGING; ISBN 978-989-758-731-3, SciTePress, pages 316-323. DOI: 10.5220/0013129400003911

in Bibtex Style

@conference{bioimaging25,
author={Eleonora Melissa and Daria Riabitch and Linda Lazzeri and Federica La Rosa and Chiara Benvenuti and Mario D’Acunto and Giovanni Bagnoni and Daniela Massi and Marco Laurino},
title={A Real-World Segmentation Model for Melanocytic and Nonmelanocytic Dermoscopic Images},
booktitle={Proceedings of the 18th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 1: BIOIMAGING},
year={2025},
pages={316-323},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013129400003911},
isbn={978-989-758-731-3},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 18th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 1: BIOIMAGING
TI - A Real-World Segmentation Model for Melanocytic and Nonmelanocytic Dermoscopic Images
SN - 978-989-758-731-3
AU - Melissa E.
AU - Riabitch D.
AU - Lazzeri L.
AU - La Rosa F.
AU - Benvenuti C.
AU - D’Acunto M.
AU - Bagnoni G.
AU - Massi D.
AU - Laurino M.
PY - 2025
SP - 316
EP - 323
DO - 10.5220/0013129400003911
PB - SciTePress