Melanoma Recognition
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985556%3A_____%2F22%3A00552810" target="_blank" >RIV/67985556:_____/22:00552810 - isvavai.cz</a>
Result on the web
<a href="http://dx.doi.org/10.5220/0000156800003124" target="_blank" >http://dx.doi.org/10.5220/0000156800003124</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.5220/0000156800003124" target="_blank" >10.5220/0000156800003124</a>
Alternative languages
Result language
angličtina
Original language name
Melanoma Recognition
Original language description
Early and reliable melanoma detection is one of today's significant challenges for dermatologists to allow successfulncancer treatment. This paper introduces multispectral rotationally invariant textural features of the Markovian type applied to effective skin cancerous lesions classification.nPresented 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 highnaccuracy 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.
Czech name
—
Czech description
—
Classification
Type
D - Article in proceedings
CEP classification
—
OECD FORD branch
20205 - Automation and control systems
Result continuities
Project
<a href="/en/project/GA19-12340S" target="_blank" >GA19-12340S: Surface material recognition under variable optical observation conditions</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2022
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Proceedings of the 17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications
ISBN
978-989-758-555-5
ISSN
2184-4321
e-ISSN
—
Number of pages
8
Pages from-to
722-729
Publisher name
Scitepress - Science and Technology Publications, Lda
Place of publication
Setúbal
Event location
Setúbal - online
Event date
Feb 6, 2022
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
—