Non-melanoma skin lesion classification using colour image data in a hierarchical K-NN classifier

L. Ballerini, R.B. Fisher, B. Aldridge, J. Rees

Research output: Chapter in Book/Report/Conference proceedingConference contribution


This paper presents an algorithm for classification of non-melanoma skin lesions based on a novel hierarchical K-Nearest Neighbors (K-NN) classifier. The K-NN classifier is simple, quick and effective. 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. Colour and texture features are extracted from skin lesions. The accuracy of the proposed hierarchical scheme is higher than 93% in discriminating cancer and pre-malignant 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 published result on non-melanoma skin cancer classification from colour images acquired by a standard camera (non-dermoscopy).
Original languageEnglish
Title of host publicationBiomedical Imaging (ISBI), 2012 9th IEEE International Symposium on
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Number of pages4
ISBN (Print)978-1-4577-1857-1
Publication statusPublished - 1 May 2012


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