A case study in feature invention for breast cancer diagnosis using X-ray scatter images

Shane M Butler, Geoffrey I Webb, Robert A Lewis

    Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

    11 Citations (Scopus)


    X-ray mammography is the current clinical method for screening for breast cancer, and like any technique, has its limitations. Several groups have reported differences in the X-ray scattering patterns of normal and tumour tissue from the breast. This gives rise to the hope that X-ray scatter analysis techniques may lead to a more accurate and cost effective method of diagnosing beast cancer which lends itself to automation. This is a particularly challenging exercise due to the inherent complexity of the information content in X-ray scatter patterns from complex hetrogenous tissue samples. We use a simple naïve Bayes classier as our classification system. High-level features are extracted from the low-level pixel data. This paper reports some preliminary results in the ongoing development of this classification method that can distinguish between the diffraction patterns of normal and cancerous tissue, with particular emphasis on the invention of features for classification.
    Original languageEnglish
    Title of host publicationAI 2003: Advances in Artificial Intelligence
    Subtitle of host publication16th Australian Conference on AI, Perth, Australia, December 3-5, 2003, Proceedings
    EditorsTamas D Gedeon, Lance Chun Che Fung
    Place of PublicationNew York NY USA
    Number of pages9
    ISBN (Electronic)9783540245810
    ISBN (Print)9783540206460
    Publication statusPublished - 2003
    EventAustralasian Joint Conference on Artificial Intelligence 2003 - Perth, Australia
    Duration: 3 Dec 20035 Dec 2003
    Conference number: 16th
    https://link.springer.com/book/10.1007/b94701 (Proceedings)

    Publication series

    NameLecture Notes in Computer Science
    ISSN (Print)0302-9743


    ConferenceAustralasian Joint Conference on Artificial Intelligence 2003
    Abbreviated titleAI 2003
    Internet address

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