Title: | A primary morphological classifier for skin lesion images |
Authors: | Macatangay, Jules Matthew A. Ruiz Jr., Conrado R. Usatine, Richard P. |
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. |
Issue Date: | 2017 |
Publisher: | Václav Skala - UNION Agency |
Document type: | konferenční příspěvek conferenceObject |
URI: | wscg.zcu.cz/WSCG2017/!!_CSRN-2701.pdf http://hdl.handle.net/11025/29545 |
ISBN: | 978-80-86943-44-2 |
ISSN: | 2464–4617 (print) 2464–4625 (CD-ROM) |
Keywords: | kožní léze;klasifikace;strojové učení;počítačové vidění |
Keywords in different language: | skin lesion;classification;machine learning;computer vision |
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. |
Rights: | © Václav Skala - UNION Agency |
Appears in Collections: | WSCG 2017: Full Papers Proceedings |
Files in This Item:
File | Description | Size | Format | |
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Macatangay.pdf | Plný text | 7,1 MB | Adobe PDF | View/Open |
Please use this identifier to cite or link to this item:
http://hdl.handle.net/11025/29545
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