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    Advanced Deep Learning for Medical Image Analysis

    Nugroho BA 2022.pdf (3.834Mb)
    Access Status
    Open access
    Authors
    Nugroho, Bayu Adhi
    Date
    2022
    Supervisor
    Hannes Herrmann
    Iain Murray
    Type
    Thesis
    Award
    PhD
    
    Metadata
    Show full item record
    Faculty
    Science and Engineering
    School
    School of Electrical Engineering, Computing and Mathematical Sciences
    URI
    http://hdl.handle.net/20.500.11937/87912
    Collection
    • Curtin Theses
    Abstract

    The application of deep learning is evolving, including in expert systems for healthcare, such as disease classification. Several challenges in the use of deep-learning algorithms in application to disease classification. The study aims to improve classification to address the problem. The thesis proposes a cost-sensitive imbalance training algorithm to address an unequal number of training examples, a two-stage Bayesian optimisation training algorithm and a dual-branch network to train a one-class classification scheme, further improving classification performance.

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