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: Contribution to conferencePaper

    20 Citations (Scopus)

    Abstract

    This paper presents an algorithm for classification of nonmelanoma
    skin lesions based on a novel hierarchical KNearest 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
    Pages358-361
    Number of pages4
    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

    Fingerprint

    Skin
    Classifiers
    Color
    Feature extraction
    Textures
    Cameras

    Cite this

    Ballerini, L., Fisher, R. B., Aldridge, B., & Rees, J. (2012). Non-melanoma skin lesion classification using colour image data in a hierarchical K-NN classifier. 358-361 . Paper presented at 9th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, Barcelona, Spain. https://doi.org/10.1109/ISBI.2012.6235558
    Ballerini, Lucia ; Fisher, Robert B. ; Aldridge, Ben ; Rees, Jonathan. / Non-melanoma skin lesion classification using colour image data in a hierarchical K-NN classifier. Paper presented at 9th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, Barcelona, Spain.4 p.
    @conference{af1cc633c9c649f7ba300e9aa999f217,
    title = "Non-melanoma skin lesion classification using colour image data in a hierarchical K-NN classifier",
    abstract = "This paper presents an algorithm for classification of nonmelanomaskin lesions based on a novel hierarchical KNearest Neighbors (K-NN) classifier. The K-NN classifier is simple, quick and effective. The hierarchical structuredecomposes 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 mostextensive published result on non-melanoma skin cancer classification from colour images acquired by a standard camera (non-dermoscopy).",
    author = "Lucia Ballerini and Fisher, {Robert B.} and Ben Aldridge and Jonathan Rees",
    note = "2012 9th IEEE International Symposium on Biomedical Imaging (ISBI); 9th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2012 ; Conference date: 02-05-2012 Through 05-05-2012",
    year = "2012",
    doi = "10.1109/ISBI.2012.6235558",
    language = "English",
    pages = "358--361",

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    Ballerini, L, Fisher, RB, Aldridge, B & Rees, J 2012, 'Non-melanoma skin lesion classification using colour image data in a hierarchical K-NN classifier', Paper presented at 9th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, Barcelona, Spain, 2/05/12 - 5/05/12 pp. 358-361 . https://doi.org/10.1109/ISBI.2012.6235558

    Non-melanoma skin lesion classification using colour image data in a hierarchical K-NN classifier. / Ballerini, Lucia; Fisher, Robert B.; Aldridge, Ben; Rees, Jonathan.

    2012. 358-361 Paper presented at 9th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, Barcelona, Spain.

    Research output: Contribution to conferencePaper

    TY - CONF

    T1 - Non-melanoma skin lesion classification using colour image data in a hierarchical K-NN classifier

    AU - Ballerini, Lucia

    AU - Fisher, Robert B.

    AU - Aldridge, Ben

    AU - Rees, Jonathan

    N1 - 2012 9th IEEE International Symposium on Biomedical Imaging (ISBI)

    PY - 2012

    Y1 - 2012

    N2 - This paper presents an algorithm for classification of nonmelanomaskin lesions based on a novel hierarchical KNearest Neighbors (K-NN) classifier. The K-NN classifier is simple, quick and effective. The hierarchical structuredecomposes 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 mostextensive published result on non-melanoma skin cancer classification from colour images acquired by a standard camera (non-dermoscopy).

    AB - This paper presents an algorithm for classification of nonmelanomaskin lesions based on a novel hierarchical KNearest Neighbors (K-NN) classifier. The K-NN classifier is simple, quick and effective. The hierarchical structuredecomposes 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 mostextensive published result on non-melanoma skin cancer classification from colour images acquired by a standard camera (non-dermoscopy).

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    DO - 10.1109/ISBI.2012.6235558

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    Ballerini L, Fisher RB, Aldridge B, Rees J. Non-melanoma skin lesion classification using colour image data in a hierarchical K-NN classifier. 2012. Paper presented at 9th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, Barcelona, Spain. https://doi.org/10.1109/ISBI.2012.6235558