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

Lucia Ballerini, Robert B. Fisher, Ben Aldridge, Jonathan Rees

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

    22 Citations (Scopus)

    Abstract

    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 publication2012 9th IEEE International Symposium on Biomedical Imaging
    Subtitle of host publicationFrom Nano to Macro - Proceedings
    Place of PublicationPiscataway
    PublisherIEEE
    Pages358-361
    Number of pages4
    ISBN (Print)9781457718588
    DOIs
    Publication statusPublished - 2012
    Event9th IEEE International Symposium on Biomedical Imaging: From Nano to Macro - Centre de Convencions Internacional de Barcelona, Barcelona, Spain
    Duration: 2 May 20125 May 2012

    Conference

    Conference9th IEEE International Symposium on Biomedical Imaging: From Nano to Macro
    Abbreviated titleISBI 2012
    CountrySpain
    CityBarcelona
    Period2/05/125/05/12

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