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,

• 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",
• title = "Integrating a Fuzzy Fitness Function in Genetic Programming to Generate Breast Tissue Segmentation Models.",
• journal = "IEEE Access",
• note = "Early Access",
• 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",
• 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