Generating Knowledge-Guided Discriminative Features Using Genetic Programming for Melanoma Detection
Created by W.Langdon from
gp-bibliography.bib Revision:1.7954
- @Article{UlAin:ieeeETCI,
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author = "Qurrat {Ul Ain} and Harith Al-Sahaf and Bing Xue and
Mengjie Zhang",
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title = "Generating Knowledge-Guided Discriminative Features
Using Genetic Programming for Melanoma Detection",
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journal = "IEEE Transactions on Emerging Topics in Computational
Intelligence",
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year = "2021",
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volume = "5",
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number = "4",
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pages = "554--569",
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month = aug,
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keywords = "genetic algorithms, genetic programming, feature
selection, feature construction, image classification,
melanoma detection",
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ISSN = "2471-285X",
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DOI = "doi:10.1109/TETCI.2020.2983426",
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size = "16 pages",
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abstract = "Melanoma is the deadliest form of skin cancer that
causes around 7percent of deaths worldwide. However,
most of the skin cancers can be cured, especially if
detected and treated early. Existing approaches have
employed various feature extraction methods, where
different types of features are used individually for
skin image classification which may not provide
sufficient information to the classification algorithm
necessary to discriminate between classes, leading to
sub-optimal performance. This study develops a novel
skin image classification method using multi-tree
genetic programming (GP). To capture local information
from gray and color skin images, Local Binary Pattern
is used in this work. In addition, for capturing global
information, variation in color within the lesion and
the skin regions, and domain-specific lesion border
shape features are extracted. GP with a multi-tree
representation is employed to use multiple types of
features. Genetic operators such as crossover and
mutation are designed accordingly in order to select a
single type of features at terminals in one tree of the
GP individual. The performance of the proposed method
is assessed using two skin image datasets having images
captured from multiple modalities, and compared with
six most commonly used classification algorithms as
well as the standard (single-tree) wrapper and embedded
GP methods. The results show that the proposed method
has significantly outperformed all these classification
methods. Being interpretable and fast in terms of the
computation time, this method can help dermatologist
identify prominent skin image features, specific to a
type of skin cancer in real-time situations.",
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notes = "Also known as \cite{9072194}",
- }
Genetic Programming entries for
Qurrat Ul Ain
Harith Al-Sahaf
Bing Xue
Mengjie Zhang
Citations