A Multi-tree Genetic Programming Representation for Melanoma Detection Using Local and Global Features
Created by W.Langdon from
gp-bibliography.bib Revision:1.8098
- @InProceedings{Ain:2018:AJCAI,
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author = "Qurrat {Ul Ain} and Harith Al-Sahaf and Bing Xue and
Mengjie Zhang",
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title = "A Multi-tree Genetic Programming Representation for
Melanoma Detection Using Local and Global Features",
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booktitle = "Australasian Joint Conference on Artificial
Intelligence",
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year = "2018",
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editor = "Tanja Mitrovic and Bing Xue and Xiaodong Li",
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volume = "11320",
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series = "LNCS",
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pages = "111--123",
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address = "Wellington, New Zealand",
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month = dec # " 11-14",
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publisher = "Springer",
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keywords = "genetic algorithms, genetic programming, image
classification, feature extraction, feature selection,
melanoma detection",
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isbn13 = "978-3-030-03990-5",
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URL = "https://link.springer.com/chapter/10.1007%2F978-3-030-03991-2_12",
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DOI = "doi:10.1007/978-3-030-03991-2_12",
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size = "13 pages",
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abstract = "Melanoma is the deadliest type of skin cancer that
accounts for nearly 75percent of deaths associated with
it. However, survival rate is high, if diagnosed at an
early stage. This study develops a novel classification
approach to melanoma detection using a multi-tree
genetic programming (GP) method. Existing approaches
have employed various feature extraction methods to
extract features from skin cancer images, where these
different types of features are used individually for
skin cancer image classification. However they remain
unable to use all these features together in a
meaningful way to achieve performance gains. In this
work, Local Binary Pattern is used to extract local
information from gray and colour images. Moreover, to
capture the global information, colour variation among
the lesion and skin regions, and geometrical border
shape features are extracted. Genetic operators such as
crossover and mutation are designed accordingly to fit
the objectives of our proposed method. The performance
of the proposed method is assessed using two skin image
datasets and compared with six commonly used
classification algorithms as well as the single tree GP
method. The results show that the proposed method
significantly outperformed all these classification
methods. Being interpretable, this method may help
dermatologist identify prominent skin image features,
specific to a type of skin cancer.",
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notes = "conf/ausai/AinAXZ18",
- }
Genetic Programming entries for
Qurrat Ul Ain
Harith Al-Sahaf
Bing Xue
Mengjie Zhang
Citations