Fuzzy Logic Color Detection: Blue Areas in Melanoma Dermoscopy Images
Fuzzy logic image analysis techniques were used to analyze three shades of blue (lavender blue, light blue, and dark blue) in dermoscopic images for melanoma detection. A logistic regression model provided up to 82.7% accuracy for melanoma discrimination for 866 images. With a support vector machines (SVM) classifier, lower accuracy was obtained for individual shades (79.9-80.1%) compared with up to 81.4% accuracy with multiple shades. All fuzzy blue logic alpha cuts scored higher than the crisp case. Fuzzy logic techniques applied to multiple shades of blue can assist in melanoma detection. These vector-based fuzzy logic techniques can be extended to other image analysis problems involving multiple colors or color shades.
M. Lingala et al., "Fuzzy Logic Color Detection: Blue Areas in Melanoma Dermoscopy Images," Computerized Medical Imaging and Graphics, vol. 38, no. 5, pp. 403-410, Elsevier Ltd, Jul 2014.
The definitive version is available at https://doi.org/10.1016/j.compmedimag.2014.03.007
Electrical and Computer Engineering
Keywords and Phrases
Dermatology; Image analysis; Oncology; Regression analysis; Support vector machines; Blue area; Dermoscopy; Dysplastic nevi; Fuzzy logic techniques; Image analysis techniques; Logistic Regression modeling; Melanoma; Vector-based fuzzy logic; Fuzzy logic; algorithm; cancer diagnosis; color discrimination; colorimetry; diagnostic accuracy; diagnostic test accuracy study; epiluminescence microscopy; human; priority journal; receiver operating characteristic; support vector machine
International Standard Serial Number (ISSN)
Article - Journal
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