| 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 |
|
| editor |
|
|
| 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 |
|
| 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 |
|