A Genetic Programming Approach to Automatically Construct Informative Attributes for Mammographic Density Classification
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gp-bibliography.bib Revision:1.7954
- @InProceedings{Ain:2022:ICDMW,
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author = "Qurrat Ul Ain and Bing Xue and Harith Al-Sahaf and
Mengjie Zhang",
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booktitle = "2022 IEEE International Conference on Data Mining
Workshops (ICDMW)",
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title = "A Genetic Programming Approach to Automatically
Construct Informative Attributes for Mammographic
Density Classification",
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year = "2022",
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pages = "378--387",
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abstract = "Breast density is widely used as an initial indicator
of developing breast cancer. At present, current
classification methods for mammographic density usually
require manual operations or expert knowledge that
makes them expensive in real-time situations. Such
methods achieve only moderate classification accuracy
due to the limited model capacity and computational
resources. In addition, most existing studies focus on
improving classification accuracy using only raw images
or the entire set of original attributes and remain
unable to identify hidden patterns or causal
information necessary to discriminate breast density
classes. It is challenging to find high-quality
knowledge when some attributes defining the data space
are redundant or irrelevant. In this study, we present
a novel attribute construction method using genetic
programming (GP) for the task of breast density
classification. To extract informative features from
the raw mammographic images, wavelet decomposition,
local binary patterns, and histogram of oriented
gradients are used to include texture, local and global
image properties. The study evaluates the goodness of
the proposed method on two benchmark real-world
mammographic image datasets and compares the results of
the proposed GP method with eight conventional
classification methods. The experimental results reveal
that the proposed method significantly outperforms most
of the commonly used classification methods in binary
and multi-class classification tasks. Furthermore, the
study shows the potential of G P for mammographic
breast density classification by interpreting evolved
attributes that highlight important breast density
characteristics.",
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keywords = "genetic algorithms, genetic programming",
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DOI = "doi:10.1109/ICDMW58026.2022.00057",
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ISSN = "2375-9259",
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month = nov,
-
notes = "Also known as \cite{10031110}",
- }
Genetic Programming entries for
Qurrat Ul Ain
Bing Xue
Harith Al-Sahaf
Mengjie Zhang
Citations