bibtype C - Conference Paper (international conference)
ARLID 0552810
utime 20250123085701.0
mtime 20220204235959.9
SCOPUS 85182934075
WOS 000777569400080
DOI 10.5220/0000156800003124
title (primary) (eng) Melanoma Recognition
specification
page_count 8 s.
media_type P
serial
ARLID cav_un_epca*0552809
ISBN 978-989-758-555-5
ISSN 2184-4321
title Proceedings of the 17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications
page_num 722-729
publisher
place Setúbal
name Scitepress - Science and Technology Publications, Lda
year 2022
editor
name1 Farinella
name2 G.M.
editor
name1 Radeva
name2 P.
editor
name1 Bouatouch
name2 K.
keyword Skin Cancer Recognition
keyword Melanoma Detection
keyword Circular Markov Random Field Model
author (primary)
ARLID cav_un_auth*0101093
name1 Haindl
name2 Michal
institution UTIA-B
full_dept (cz) Rozpoznávání obrazu
full_dept (eng) Department of Pattern Recognition
department (cz) RO
department (eng) RO
full_dept Department of Pattern Recognition
garant A
fullinstit Ústav teorie informace a automatizace AV ČR, v. v. i.
author
ARLID cav_un_auth*0101239
name1 Žid
name2 Pavel
institution UTIA-B
full_dept (cz) Rozpoznávání obrazu
full_dept Department of Pattern Recognition
department (cz) RO
department RO
full_dept Department of Pattern Recognition
fullinstit Ústav teorie informace a automatizace AV ČR, v. v. i.
source
url http://library.utia.cas.cz/separaty/2022/RO/haindl-0552810.pdf
cas_special
project
project_id GA19-12340S
agency GA ČR
country CZ
ARLID cav_un_auth*0376011
abstract (eng) Early and reliable melanoma detection is one of today's significant challenges for dermatologists to allow successful\ncancer 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 high\naccuracy 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.
action
ARLID cav_un_auth*0423777
name International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2022) /17./
dates 20220206
mrcbC20-s 20220208
place Setúbal - online
country PT
RIV BD
FORD0 20000
FORD1 20200
FORD2 20205
reportyear 2022
num_of_auth 2
mrcbC52 4 A sml 4as 20231122150333.5
presentation_type PR
inst_support RVO:67985556
permalink http://hdl.handle.net/11104/0327904
confidential S
contract
name Consent to publish and coypright transfer
date 20211227
article_num 268
arlyear 2022
mrcbTft \nSoubory v repozitáři: haindl-0552810-22VISAPP_copyright.pdf
mrcbU14 85182934075 SCOPUS
mrcbU24 PUBMED
mrcbU34 000777569400080 WOS
mrcbU63 cav_un_epca*0552809 Proceedings of the 17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications Scitepress - Science and Technology Publications, Lda 2022 Setúbal 722 729 978-989-758-555-5 2184-4321
mrcbU67 Farinella G.M. 340
mrcbU67 Radeva P. 340
mrcbU67 Bouatouch K. 340