Melanoma Recognition

Michal Haindl, Pavel Žid

2022

Abstract

Early and reliable melanoma detection is one of today's significant challenges for dermatologists to allow successful cancer treatment. This paper introduces multispectral rotationally invariant textural features of the Markovian type applied to effective skin cancerous lesions classification. Presented texture features are inferred from the descriptive multispectral circular wide-sense Markov model. Unlike the alternative texture-based recognition methods, mainly using different discriminative textural descriptions, our textural representation is fully descriptive multispectral and rotationally invariant. The presented method achieves high accuracy for skin lesion categorization. We tested our classifier on the open-source dermoscopic ISIC database, containing 23 901 benign or malignant lesions images, where the classifier outperformed several deep neural network alternatives while using smaller training data.

Paper Citation

in Harvard Style

Haindl M. and Žid P. (2022). Melanoma Recognition. In Proceedings of the 17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2022) - Volume 4: VISAPP; ISBN 978-989-758-555-5, SciTePress, pages 722-729. DOI: 10.5220/0010936400003124

in Bibtex Style

@conference{visapp22,
author={Michal Haindl and Pavel Žid},
title={Melanoma Recognition},
booktitle={Proceedings of the 17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2022) - Volume 4: VISAPP},
year={2022},
pages={722-729},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010936400003124},
isbn={978-989-758-555-5},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2022) - Volume 4: VISAPP
TI - Melanoma Recognition
SN - 978-989-758-555-5
AU - Haindl M.
AU - Žid P.
PY - 2022
SP - 722
EP - 729
DO - 10.5220/0010936400003124
PB - SciTePress