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AI’s potential for Artificial Phantom Mimicking Tissue of the Human Breast Electrical Properties

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  • Additional Information
    • Contributors:
      Université Claude Bernard Lyon 1 (UCBL); Université de Lyon; Centre de Recherche en Acquisition et Traitement de l'Image pour la Santé (CREATIS); Université de Lyon-Université de Lyon-Institut National des Sciences Appliquées de Lyon (INSA Lyon); Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université Jean Monnet - Saint-Étienne (UJM)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)
    • Publication Information:
      HAL CCSD
    • Publication Date:
      2021
    • Collection:
      Université Jean Monnet – Saint-Etienne: HAL
    • Subject Terms:
    • Abstract:
      National audience ; In this study, we present an assessment method of Early Breast Cancer Detection based on Artificial Intelligence (AI). This method includes prediction, setting and measurement of several human breast phantom mixtures for Microwave Imaging (MWI). These developed Breast Phantoms have realistic dielectric properties (important for Radar Microwave Imaging Systems & Ethical Non-Animal-Testing). We Developed Heterogeneous mimicking tissue architecture for mimicking Breast Tissues (Fat, Regular Fat, Gland, Body Fluid, Blood, Muscle-Tumor, Skin Dry, and Skin Wet) and tested using ultrawideband frequency (0.2 GHz-4.5 GHz) by a modern Vector Network Analyzer. Our qualitative and quantitative research includes the methodology and results for using the material's dielectric parameters and AI algorithms for emulation of real Breast Tissues for Microwave Imaging Pre-clinical trials.
    • Relation:
      hal-03298725; https://hal.science/hal-03298725; https://hal.science/hal-03298725/document; https://hal.science/hal-03298725/file/RJCIA_2021_paper_10.pdf
    • Online Access:
      https://hal.science/hal-03298725
      https://hal.science/hal-03298725/document
      https://hal.science/hal-03298725/file/RJCIA_2021_paper_10.pdf
    • Rights:
      info:eu-repo/semantics/OpenAccess
    • Accession Number:
      edsbas.2408DE0D