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Melanoma Recognition
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SYSNO ASEP 0552810 Document Type C - Proceedings Paper (int. conf.) R&D Document Type Conference Paper Title Melanoma Recognition Author(s) Haindl, Michal (UTIA-B) RID, ORCID
Žid, Pavel (UTIA-B) RID, ORCIDNumber of authors 2 Article number 268 Source Title 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.M. ; Radeva P. ; Bouatouch K. - ISSN 2184-4321 - ISBN 978-989-758-555-5 Pages s. 722-729 Number of pages 8 s. Publication form Print - P Action International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2022) /17./ Event date 06.02.2022 - 08.02.2022 VEvent location Setúbal - online Country PT - Portugal Event type WRD Language eng - English Country PT - Portugal Keywords Skin Cancer Recognition ; Melanoma Detection ; Circular Markov Random Field Model Subject RIV BD - Theory of Information OECD category Automation and control systems R&D Projects GA19-12340S GA ČR - Czech Science Foundation (CSF) Institutional support UTIA-B - RVO:67985556 DOI 10.5220/0000156800003124 Annotation 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.Workplace Institute of Information Theory and Automation Contact Markéta Votavová, votavova@utia.cas.cz, Tel.: 266 052 201. Year of Publishing 2022
Number of the records: 1