|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.|
|Publisher:||Václav Skala - UNION Agency|
|Document type:||konferenční příspěvek|
|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|
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