Genetic Programming for Feature Construction in Breast Ultrasound Tumor Classification
Created by W.Langdon from
gp-bibliography.bib Revision:1.8344
- @InProceedings{Perales-Garcia:2024:GMEPE,
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author = "M. A. Perales-Garcia and W. Gomez-Flores",
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title = "Genetic Programming for Feature Construction in Breast
Ultrasound Tumor Classification",
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booktitle = "2024 Global Medical Engineering Physics Exchanges/ Pan
American Health Care Exchanges (GMEPE/PAHCE)",
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year = "2024",
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month = apr,
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keywords = "genetic algorithms, genetic programming, cancer,
Weight measurement, Breast tumours, Computational
modelling, Ultrasonography, Syntactics, Feature
extraction, breast ultrasound, tumour classification,
feature construction",
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ISSN = "2327-817X",
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DOI = "
doi:10.1109/GMEPE/PAHCE62423.2024.10534649",
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abstract = "We present a feature construction approach based on
Genetic Programming (GP) for classifying breast tumours
into benign and malignant cases on ultrasonography. GP
is an evolutionary algorithm that codifies potential
solutions in syntax trees, representing mathematical
expressions that combine the inputs (i.e.,
morphological and texture features) using arithmetic
operators and mathematical functions to construct new
features that increase the descriptive power of the
original ones. The experimental results show that the
GP-based method achieves an accuracy of 87percent with
four constructed features. It is compared against two
feature selection techniques, attaining 87percent and
88percent accuracy with 86 and 13 selected
characteristics, respectively, and two deep networks
proposed for breast ultrasound (BUS) classification,
reaching 82percent and 84percent accuracy each. Thus,
using a reduced feature set, the proposed method is an
adequate alternative for distinguishing between benign
and malignant breast tumours on BUS images.",
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notes = "Also known as \cite{10534649}",
- }
Genetic Programming entries for
M A Perales-Garcia
Wilfrido Gomez-Flores
Citations