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This chapter proposes a novel hierarchical classification system based on the K-Nearest Neighbors (K-NN) model and its application to non-melanoma skin lesion classification. Color and texture features are extracted from skin lesion images. The hierarchical structure decomposes the classification task into a set of simpler problems, one at each node of the classification. Feature selection is embedded in the hierarchical framework that chooses the most relevant feature subsets at each node of the hierarchy. The accuracy of the proposed hierarchical scheme is higher than 93 % in discriminating cancer and potential at risk lesions from benign lesions, and it reaches an overall classification accuracy of 74 % over five common classes of skin lesions, including two non-melanoma cancer types. This is the most extensive known result on non-melanoma skin cancer classification using color and texture information from images acquired by a standard camera (non-dermoscopy).
|Title of host publication||Color Medical Image Analysis|
|Editors||M. Emre Celebi, Gerald Schaefer|
|Number of pages||24|
|Publication status||Published - 2013|
|Name||Lecture Notes in Computational Vision and Biomechanics|
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- 1 Finished
DERMOFIT: A cognitive prosthesis to aid focal skin lesion diagnosis
Fisher, B. & Rees, J.
15/09/08 → 14/09/11