Improving Classification in Skin Lesion Analysis Through Segmentation

Mirco Gallazzi, Anwar Ur Rehman, Silvia Corchs, Ignazio Gallo

2025

Abstract

Deep Learning plays a vital role in medical imaging, especially in classification and segmentation tasks essential for diagnosing diseases from images. However, current methods often struggle to differentiate visually similar classes and accurately delineate lesion boundaries. This study builds on prior findings of classification limitations, investigating whether segmentation can improve classification performance for skin lesion analysis with Transformer-based models. We benchmarked the segmentation capabilities of the Swin Transformer, YOLOv8, and DeepLabV3 architectures on the HAM dataset, which contains 10,015 images across seven skin lesion classes. Swin outperformed others in segmentation, achieving an intersection over union of 82.75%, while YOLOv8 achieved 77.0%. However, classification experiments using classification datasets after segmenting and cropping the lesion of interest did not produce the expected improvements, with classification accuracy showing slight drops in the segmented data. For example, on the original HAM dataset, the model achieved a Test Accuracy (TA) of 84.64%, while Swin trained on segmented data showed a slight decline to a TA of 84.13%. These findings suggest that segmentation alone may not effectively support classification. Based on this, we propose future research into a sequential transfer learning approach, where segmentation knowledge could be progressively transferred to improve classification.

Paper Citation

in Harvard Style

Gallazzi M., Rehman A., Corchs S. and Gallo I. (2025). Improving Classification in Skin Lesion Analysis Through Segmentation. In Proceedings of the 14th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM; ISBN 978-989-758-730-6, SciTePress, pages 696-703. DOI: 10.5220/0013247900003905

in Bibtex Style

@conference{icpram25,
author={Mirco Gallazzi and Anwar Rehman and Silvia Corchs and Ignazio Gallo},
title={Improving Classification in Skin Lesion Analysis Through Segmentation},
booktitle={Proceedings of the 14th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM},
year={2025},
pages={696-703},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013247900003905},
isbn={978-989-758-730-6},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 14th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM
TI - Improving Classification in Skin Lesion Analysis Through Segmentation
SN - 978-989-758-730-6
AU - Gallazzi M.
AU - Rehman A.
AU - Corchs S.
AU - Gallo I.
PY - 2025
SP - 696
EP - 703
DO - 10.5220/0013247900003905
PB - SciTePress