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- W3036454057 abstract "In the context of increasing interest in computer-assisted diagnosis for skin lesion images and mobile applications to be used in real life settings, we propose a combined desktop-smartphone solution for dermatological image classification. Hierarchical agglomerative and divisive clustering are both implemented as methods of cluster analysis, with the RGB color histogram as descriptor for a global image analysis. The cosine similarity is employed for classifying the query image in one of the available clusters, characterized by their centroids. The solution has been tested with a public database of dermoscopic images, with an overall accuracy of 0.73, 95%CI (0.58;0.85)." @default.
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- W3036454057 date "2020-06-16" @default.
- W3036454057 modified "2023-10-18" @default.
- W3036454057 title "Hierarchical Clustering for Image Classification in Dermatology: Towards Mobile Deploying." @default.
- W3036454057 doi "https://doi.org/10.3233/shti200413" @default.
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