Integrating a Fuzzy Fitness Function in Genetic Programming to Generate Breast Tissue Segmentation Models.
Created by W.Langdon from
gp-bibliography.bib Revision:1.8344
- @Article{Valencia-Hernandez:ACCESS,
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author = "I. Valencia-Hernandez and C. A. Reyes-Garcia and
Alicia Morales-Reyes and G. C. Lopez-Armas and
J. A. Fuentes-Tomas and E Mezura-Montes",
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title = "Integrating a Fuzzy Fitness Function in Genetic
Programming to Generate Breast Tissue Segmentation
Models.",
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journal = "IEEE Access",
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note = "Early Access",
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keywords = "genetic algorithms, genetic programming, Image
segmentation, Breast, Mammography, Breast tissue, Fats,
Medical diagnostic imaging, Computational modelling,
Feature extraction, Accuracy, Fuzzy fitness function,
Medical Image Segmentation, Breast density",
-
ISSN = "2169-3536",
-
DOI = "
doi:10.1109/ACCESS.2025.3565462",
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abstract = "Genetic programming (GP) and fuzzy logic to
automatically segment mammography images. GP allows the
evolution of optimised segmentation models, guided by a
fuzzy logic-based fitness function that incorporates
medical criteria to improve the consistency and
accuracy of the segmentation process. Unlike
conventional approaches, this function optimises the
segmentation and provides a descriptive representation
of the breast tissue, allowing a closer evaluation to
that performed by specialists. The proposed method was
evaluated in the INbreast and BCDR databases, obtaining
a Jaccard index of 0.82 and 0.78, respectively, and a
comparative analysis was performed using ROC curves,
reaching an AUC of 0.91 in INbreast and 0.89 in BCDR,
demonstrating the model's ability to discriminate
between fibroglandular and fat tissue. Its performance
was compared with state-of-the-art methods, such as
LIBRA, hybrid segmentation with Fuzzy C-Means, and
NASGP-Net, showing that integrating fuzzy logic in
genetic programming to lead the search allows
competitive results with a lower computational burden.
These results demonstrate the impact of fuzzy fitness
functions in the evolution of segmentation models,
highlighting the effectiveness of this approach in
improving the segmentation and classification of
medical images, in addition to the descriptive
capabilities inherent to the fuzzy fitness function.",
-
notes = "Also known as \cite{10979870}",
- }
Genetic Programming entries for
I Valencia-Hernandez
C A Reyes-Garcia
Alicia Morales-Reyes
G C Lopez-Armas
Jose-Antonio Fuentes-Tomas
Efren Mezura-Montes
Citations