Full metadata record
DC pole | Hodnota | Jazyk |
---|---|---|
dc.contributor.author | Macatangay, Jules Matthew A. | |
dc.contributor.author | Ruiz Jr., Conrado R. | |
dc.contributor.author | Usatine, Richard P. | |
dc.contributor.editor | Skala, Václav | |
dc.date.accessioned | 2018-04-11T08:22:23Z | - |
dc.date.available | 2018-04-11T08:22:23Z | - |
dc.date.issued | 2017 | |
dc.identifier.citation | WSCG 2017: full papers proceedings: 25th International Conference in Central Europe on Computer Graphics, Visualization and Computer Visionin co-operation with EUROGRAPHICS Association, p. 55-64. | en |
dc.identifier.isbn | 978-80-86943-44-2 | |
dc.identifier.issn | 2464–4617 (print) | |
dc.identifier.issn | 2464–4625 (CD-ROM) | |
dc.identifier.uri | wscg.zcu.cz/WSCG2017/!!_CSRN-2701.pdf | |
dc.identifier.uri | http://hdl.handle.net/11025/29545 | |
dc.description.abstract | Classifying skin lesions, abnormal changes in skin, into their morphologies is the first step in diagnosing skin diseases. In dermatology, morphology is a categorization of a skin lesion’s structure and appearance. Rather than directly classifying skin diseases, this research aims to explore classifying skin lesion images into primary morphologies. For preprocessing, k-means clustering for image segmentation and illumination equalization were applied. Additionally, features utilized considered color, texture, and shape. For classification, k-Nearest Neighbors, Decision Trees, Multilayer Perceptron, and Support Vector Machines were used. To evaluate the prototype, 10-fold cross validation was applied over a dataset assembled from online resources. In experimentation, the morphologies considered were macule, nodule, papule, and plaque. Moreover, different feature subsets were tested through feature selection experiments. Experimental results on the 4-class and 3-class tests show that of the classifiers selected, Decision Trees were best, having a Cohen’s kappa of 0.503 and 0.558 respectively. | en |
dc.format | 10 s. | cs |
dc.format.mimetype | application/pdf | |
dc.language.iso | en | en |
dc.publisher | Václav Skala - UNION Agency | cs |
dc.relation.ispartofseries | WSCG 2017: full papers proceedings | en |
dc.rights | © Václav Skala - UNION Agency | en |
dc.subject | kožní léze | cs |
dc.subject | klasifikace | cs |
dc.subject | strojové učení | cs |
dc.subject | počítačové vidění | cs |
dc.title | A primary morphological classifier for skin lesion images | en |
dc.type | konferenční příspěvek | cs |
dc.type | conferenceObject | en |
dc.rights.access | openAccess | en |
dc.type.version | publishedVersion | en |
dc.subject.translated | skin lesion | en |
dc.subject.translated | classification | en |
dc.subject.translated | machine learning | en |
dc.subject.translated | computer vision | en |
dc.type.status | Peer-reviewed | en |
Vyskytuje se v kolekcích: | WSCG 2017: Full Papers Proceedings |
Soubory připojené k záznamu:
Soubor | Popis | Velikost | Formát | |
---|---|---|---|---|
Macatangay.pdf | Plný text | 7,1 MB | Adobe PDF | Zobrazit/otevřít |
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http://hdl.handle.net/11025/29545
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