Melanoma Recognition

0552810 - ÚTIA 2022 RIV PT eng C - Conference Paper (international conference)
Haindl, Michal - Žid, Pavel
Melanoma Recognition.
Proceedings of the 17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications. Setúbal: Scitepress - Science and Technology Publications, Lda, 2022 - (Farinella, G.; Radeva, P.; Bouatouch, K.), s. 722-729, č. článku 268. ISBN 978-989-758-555-5. ISSN 2184-4321.
[International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2022) /17./. Setúbal - online (PT), 06.02.2022-08.02.2022]
R&D Projects: GA ČR(CZ) GA19-12340S
Institutional support: RVO:67985556
Keywords : Skin Cancer Recognition * Melanoma Detection * Circular Markov Random Field Model
OECD category: Automation and control systems
http://library.utia.cas.cz/separaty/2022/RO/haindl-0552810.pdf

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.
Permanent Link: http://hdl.handle.net/11104/0327904