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,

• author = "M. A. Perales-Garcia and W. Gomez-Flores",
• title = "Genetic Programming for Feature Construction in Breast Ultrasound Tumor Classification",
• booktitle = "2024 Global Medical Engineering Physics Exchanges/ Pan American Health Care Exchanges (GMEPE/PAHCE)",
• year = "2024",
• month = apr,
• keywords = "genetic algorithms, genetic programming, cancer, Weight measurement, Breast tumours, Computational modelling, Ultrasonography, Syntactics, Feature extraction, breast ultrasound, tumour classification, feature construction",
• ISSN = "2327-817X",
• DOI = "doi:10.1109/GMEPE/PAHCE62423.2024.10534649",
• 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.",
• notes = "Also known as \cite{10534649}",

}

Genetic Programming entries for M A Perales-Garcia Wilfrido Gomez-Flores

Citations