Image Classification with Genetic Programming: Building a Stage 1 Computer Aided Detector for Breast Cancer
Created by W.Langdon from
gp-bibliography.bib Revision:1.8010
- @InCollection{Ryan:2015:hbgpa,
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author = "Conor Ryan and Jeannie Fitzgerald and
Krzysztof Krawiec and David Medernach",
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title = "Image Classification with Genetic Programming:
Building a Stage 1 Computer Aided Detector for Breast
Cancer",
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booktitle = "Handbook of Genetic Programming Applications",
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publisher = "Springer",
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year = "2015",
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editor = "Amir H. Gandomi and Amir H. Alavi and Conor Ryan",
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chapter = "10",
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pages = "245--287",
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keywords = "genetic algorithms, genetic programming",
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isbn13 = "978-3-319-20882-4",
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DOI = "doi:10.1007/978-3-319-20883-1_10",
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abstract = "This chapter describes a general approach for image
classification using Genetic Programming (GP) and
demonstrates this approach through the application of
GP to the task of stage 1 cancer detection in digital
mammograms. We detail an automated work-flow that
begins with image processing and culminates in the
evolution of classification models which identify
suspicious segments of mammograms. Early detection of
breast cancer is directly correlated with survival of
the disease and mammography has been shown to be an
effective tool for early detection, which is why many
countries have introduced national screening programs.
However, this presents challenges, as such programs
involve screening a large number of women and thus
require more trained radiologists at a time when there
is a shortage of these professionals in many
countries.Also, as mammograms are difficult to read and
radiologists typically only have a few minutes
allocated to each image, screening programs tend to be
conservative: involving many callbacks which increase
both the workload of the radiologists and the stress
and worry of patients.Fortunately, the relatively
recent increase in the availability of mammograms in
digital form means that it is now much more feasible to
develop automated systems for analysing mammograms.
Such systems, if successful could provide a very
valuable second reader function.We present a work-flow
that begins by processing digital mammograms to segment
them into smaller sub-images and to extract features
which describe textural aspects of the breast. The most
salient of these features are then used in a GP system
which generates classifiers capable of identifying
which particular segments may have suspicious areas
requiring further investigation. An important objective
of this work is to evolve classifiers which detect as
many cancers as possible but which are not overly
conservative. The classifiers give results of 100 %
sensitivity and a false positive per image rating of
just 0.33, which is better than prior work. Not only
this, but our system can use GP as part of a feedback
loop, to both select and help generate further
features.",
- }
Genetic Programming entries for
Conor Ryan
Jeannie Fitzgerald
Krzysztof Krawiec
David Medernach
Citations