Multi-objective breast cancer classification by using multi-expression programming
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- @Article{journals/apin/DiosanA15,
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author = "Laura Diosan and Anca Andreica",
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title = "Multi-objective breast cancer classification by using
multi-expression programming",
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journal = "Applied Intelligence",
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year = "2015",
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volume = "43",
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number = "3",
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pages = "499--511",
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month = oct,
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keywords = "genetic algorithms, genetic programming,
multi-objective optimization, Breast cancer",
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bibdate = "2015-09-15",
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bibsource = "DBLP,
http://dblp.uni-trier.de/db/journals/apin/apin43.html#DiosanA15",
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URL = "https://rdcu.be/c9fkd",
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DOI = "doi:10.1007/s10489-015-0668-8",
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size = "13 pages",
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abstract = "Despite many years of research, breast cancer
detection is still a difficult, but very important
problem to be solved. An automatic diagnosis system
could establish whether a mammography presents tumours
or belongs to a healthy patient and could offer, in
this way, a second opinion to a radiologist that tries
to establish a diagnosis. We therefore propose a system
that could contribute to lowering both the costs and
the work of an imaging diagnosis centre of breast
cancer and in addition to increase the trust level in
that diagnosis. We present a multi-objective
evolutionary approach based on Multi-Expression
Programming—a linear Genetic Programming
method—that could classify a mammogram starting from
a raw image of the breast. The processed images are
represented through Histogram of Oriented Gradients and
Kernel Descriptors since these image features have been
reported as being very efficient in the image
recognition scientific community and they have not been
applied to mammograms before. Numerical experiments are
performed on freely available datasets consisting of
normal and abnormal film-based and digital mammograms
and show the efficiency of the proposed decision
support system",
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notes = "Department of Computer Science, Babes-Bolyai
University, Cluj-Napoca, Romania",
- }
Genetic Programming entries for
Laura Diosan
Anca Andreica
Citations