Profile Norhene GARGOURI

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Norhene GARGOURI

  • curriculum vitae
    • First Name
    Norhene
    Last Name
    GARGOURI
    Address
    Technopole of Sfax, PO Box 275, Sakiet Ezzit, 3021 Sfax - Tunisia
    Zip code
    3038
    Biography
    Dr. Norhène Gargouri Ben Ayed is an Associate Professor at the Digital Research Center of Sfax in Tunisia. She is a member of the SM@RT Laboratory and contributes to the Computers Imaging and Electronics Systems (CIELS) research team. Her research focuses on medical image processing, where she integrates advanced techniques such as deep learning, machine learning, and artificial neural networks. Dr. Gargouri has authored over 25 scientific publications, with more than 260 citations. Her notable contributions include breast cancer anomaly detection using possibility theory and clustering paradigms, as well as the development of automatic breast computer-aided diagnosis (CAD) systems based on weighted feature fusion and deep convolutional neural networks. In addition to her research activities, she serves as the President of the Association for Scientific Research (ABORS), reflecting her dedication to scientific advancement and collaboration. Dr. Gargouri holds a Professional Doctorate in Engineering (PDEng) from the National Engineering School of Sfax, where she specialized in electrical engineering. She has also earned her University Habilitation, qualifying her to supervise doctoral research.
    Research Interests
    IA, Medical image processing
    Scientific publications
    Title Cited by Year
    Breast cancer anomaly detection based on the possibility theory with a clustering paradigm
    JF Elleuch, MZ Mehdi, M Belaaj, NG Benayed, D Sellami, A Damak
    Biomedical Signal Processing and Control 79, 104043, 2023

    		 12 2023
    Entropy-based traffic flow labeling for CNN-based traffic congestion prediction from meta-parameters
    MZ Mehdi, HM Kammoun, NG Benayed, D Sellami, AD Masmoudi
    IEEE Access 10, 16123-16133, 2022

    		 54 2022
    An automatic breast computer‐aided diagnosis scheme based on a weighted fusion of relevant features and a deep CNN classifier
    N Gargouri, R Mokni, A Damak, D Sellami, R Abid
    IET Image Processing 16 (12), 3394-3406, 2022

    		 8 2022
    System for coronavirus classification based on a new textural descriptor
    F Boubakri, N Gargouri, AD Masmoudi
    2022 IEEE 21st international Ccnference on Sciences and Techniques of …, 2022

    		 1 2022
    Microcalcification detection using k-means based clustering within a possibility theory framework
    MZ Mehdi, JF Elleuch, NG Benayed, M Belaaj, D Sellami, A Damak
    2022 8th International Conference on Control, Decision and Information …, 2022

    		 1 2022