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Naive Bayes learning of dermoscopy images
Conference series
Author
Surówka Grzegorz
Ogorzałek Maciej
Editor
Volume
2
Book title / Journal title
Artificial Intelligence and Soft Computing : 18th International Conference, ICAISC 2019, Zakopane, Poland, June 16-20, 2019, proceedings
Place of publication: Publisher
Cham : Springer
Pages
294-304
ISBN
978-3-030-20914-8
eISBN
978-3-030-20915-5
Series
Lecture Notes in Artificial Intelligence
Serie's ISSN
0302-9743
Serie's eISSN
1611-3349
Number of serie
11509
Language
English
Book language / Journal language
English
Abstract in English
We show naive Bayes models of the melanoma skin cancer represented by dermoscopy images from two different repositories. The dermoscopy images of each data set are recursively analyzed by the Mallat wavelet tree transform to extract a set of spatio-frequency filters, which are used to build energy based (rotation invariant) features. Such classifiers are built in different wavelet bases and (for one data set) in three image resolutions. Those simple models show varying classification performance, but some wavelet bases are preferable to differentiate between malicious (melanoma) and benign (displastic nevus) lesions and keep it in reduced image resolutions. The presented research contributes to the feature extraction (wrapper) methods.
Conference
18th International Conference on Artificial Intelligence and Soft Computing (ICAISC2019)
Conference short name
ICAISC
Conference start date
2019-06-16
End date conference
2019-06-20
Conference city
Zakopane
Conference country
Polska
Conference type
international
Affiliation
Wydział Fizyki, Astronomii i Informatyki Stosowanej : Instytut Informatyki Stosowanej
Scopus© citations
1
| cris.lastimport.wos | 2024-04-09T19:21:51Z | |
| dc.abstract.en | We show naive Bayes models of the melanoma skin cancer represented by dermoscopy images from two different repositories. The dermoscopy images of each data set are recursively analyzed by the Mallat wavelet tree transform to extract a set of spatio-frequency filters, which are used to build energy based (rotation invariant) features. Such classifiers are built in different wavelet bases and (for one data set) in three image resolutions. Those simple models show varying classification performance, but some wavelet bases are preferable to differentiate between malicious (melanoma) and benign (displastic nevus) lesions and keep it in reduced image resolutions. The presented research contributes to the feature extraction (wrapper) methods. | pl |
| dc.affiliation | Wydział Fizyki, Astronomii i Informatyki Stosowanej : Instytut Informatyki Stosowanej | pl |
| dc.conference | 18th International Conference on Artificial Intelligence and Soft Computing (ICAISC2019) | |
| dc.conference.city | Zakopane | |
| dc.conference.country | Polska | |
| dc.conference.datefinish | 2019-06-20 | |
| dc.conference.datestart | 2019-06-16 | |
| dc.conference.indexscopus | true | |
| dc.conference.indexwos | true | |
| dc.conference.shortcut | ICAISC | |
| dc.contributor.author | Surówka, Grzegorz - 100453 | pl |
| dc.contributor.author | Ogorzałek, Maciej - 102456 | pl |
| dc.contributor.editor | Rutkowski, Leszek | pl |
| dc.contributor.editor | Scherer, Rafał | pl |
| dc.contributor.editor | Korytkowski, Marcin | pl |
| dc.contributor.editor | Pedrycz, Witold | pl |
| dc.contributor.editor | Tadeusiewicz, Ryszard | pl |
| dc.contributor.editor | Zurada, Jacek M. | pl |
| dc.date.accessioned | 2020-03-12T13:40:35Z | |
| dc.date.available | 2020-03-12T13:40:35Z | |
| dc.date.issued | 2019 | pl |
| dc.description.conftype | international | pl |
| dc.description.physical | 294-304 | pl |
| dc.description.publication | 0,6 | pl |
| dc.description.series | Lecture Notes in Artificial Intelligence | |
| dc.description.seriesnumber | 11509 | |
| dc.description.volume | 2 | pl |
| dc.identifier.doi | 10.1007/978-3-030-20915-5_27 | pl |
| dc.identifier.eisbn | 978-3-030-20915-5 | pl |
| dc.identifier.isbn | 978-3-030-20914-8 | pl |
| dc.identifier.project | ROD UJ / O | pl |
| dc.identifier.serieseissn | 1611-3349 | |
| dc.identifier.seriesissn | 0302-9743 | |
| dc.identifier.uri | https://ruj.uj.edu.pl/xmlui/handle/item/151709 | |
| dc.language | eng | pl |
| dc.language.container | eng | pl |
| dc.pubinfo | Cham : Springer | pl |
| dc.publisher.ministerial | Springer | pl |
| dc.rights | Dodaję tylko opis bibliograficzny | * |
| dc.rights.licence | Bez licencji otwartego dostępu | |
| dc.source.integrator | false | |
| dc.subtype | ConferenceProceedings | pl |
| dc.title | Naive Bayes learning of dermoscopy images | pl |
| dc.title.container | Artificial Intelligence and Soft Computing : 18th International Conference, ICAISC 2019, Zakopane, Poland, June 16-20, 2019, proceedings | pl |
| dc.type | BookSection | pl |
| dspace.entity.type | Publication |
cris.lastimport.wos
2024-04-09T19:21:51Z dc.abstract.enpl
We show naive Bayes models of the melanoma skin cancer represented by dermoscopy images from two different repositories. The dermoscopy images of each data set are recursively analyzed by the Mallat wavelet tree transform to extract a set of spatio-frequency filters, which are used to build energy based (rotation invariant) features. Such classifiers are built in different wavelet bases and (for one data set) in three image resolutions. Those simple models show varying classification performance, but some wavelet bases are preferable to differentiate between malicious (melanoma) and benign (displastic nevus) lesions and keep it in reduced image resolutions. The presented research contributes to the feature extraction (wrapper) methods. dc.affiliationpl
Wydział Fizyki, Astronomii i Informatyki Stosowanej : Instytut Informatyki Stosowanej dc.conference
18th International Conference on Artificial Intelligence and Soft Computing (ICAISC2019) dc.conference.city
Zakopane dc.conference.country
Polska dc.conference.datefinish
2019-06-20 dc.conference.datestart
2019-06-16 dc.conference.indexscopus
true dc.conference.indexwos
true dc.conference.shortcut
ICAISC dc.contributor.authorpl
Surówka, Grzegorz - 100453 dc.contributor.authorpl
Ogorzałek, Maciej - 102456 dc.contributor.editorpl
Rutkowski, Leszek dc.contributor.editorpl
Scherer, Rafał dc.contributor.editorpl
Korytkowski, Marcin dc.contributor.editorpl
Pedrycz, Witold dc.contributor.editorpl
Tadeusiewicz, Ryszard dc.contributor.editorpl
Zurada, Jacek M. dc.date.accessioned
2020-03-12T13:40:35Z dc.date.available
2020-03-12T13:40:35Z dc.date.issuedpl
2019 dc.description.conftypepl
international dc.description.physicalpl
294-304 dc.description.publicationpl
0,6 dc.description.series
Lecture Notes in Artificial Intelligence dc.description.seriesnumber
11509 dc.description.volumepl
2 dc.identifier.doipl
10.1007/978-3-030-20915-5_27 dc.identifier.eisbnpl
978-3-030-20915-5 dc.identifier.isbnpl
978-3-030-20914-8 dc.identifier.projectpl
ROD UJ / O dc.identifier.serieseissn
1611-3349 dc.identifier.seriesissn
0302-9743 dc.identifier.uri
https://ruj.uj.edu.pl/xmlui/handle/item/151709 dc.languagepl
eng dc.language.containerpl
eng dc.pubinfopl
Cham : Springer dc.publisher.ministerialpl
Springer dc.rights*
Dodaję tylko opis bibliograficzny dc.rights.licence
Bez licencji otwartego dostępu dc.source.integrator
false dc.subtypepl
ConferenceProceedings dc.titlepl
Naive Bayes learning of dermoscopy images dc.title.containerpl
Artificial Intelligence and Soft Computing : 18th International Conference, ICAISC 2019, Zakopane, Poland, June 16-20, 2019, proceedings dc.typepl
BookSection dspace.entity.type
Publication Affiliations
Wydział Fizyki, Astronomii i Informatyki Stosowanej
Surówka, Grzegorz
Ogorzałek, Maciej
No affiliation
Rutkowski, Leszek
Scherer, Rafał
Korytkowski, Marcin
Pedrycz, Witold
Tadeusiewicz, Ryszard
Zurada, Jacek M.
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