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Authors: Mohamed Amine Mezghich ; Dorsaf Hmida ; Taha Nahdi and Faouzi Ghorbel

Affiliation: GRIFT Research Group, CRISTAL laboratory, ENSI, Tunisia

Keyword(s): Complex Moments, Invariant Descriptors, Stability, Completeness, Deep Learning, Classification.

Abstract: In this paper, we intent to present an improved VGG16 deep learning model based on an invariant and complete set of descriptors constructed by a linear combination of complex moments. First, the invariant features are studied to highlight it’s stability and completeness properties over rigid transformations, noise and non rigid transformations. Then our proposed method to inject this family to the well know deep leaning VGG16 model is presented. Experimental results are satisfactory and the model accuracy is improved.

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Paper citation in several formats:
Amine Mezghich, M.; Hmida, D.; Nahdi, T. and Ghorbel, F. (2024). An Improved VGG16 Model Based on Complex Invariant Descriptors for Medical Images Classification. In Proceedings of the 13th International Conference on Pattern Recognition Applications and Methods - ICPRAM; ISBN 978-989-758-684-2; ISSN 2184-4313, SciTePress, pages 444-452. DOI: 10.5220/0012467800003654

@conference{icpram24,
author={Mohamed {Amine Mezghich}. and Dorsaf Hmida. and Taha Nahdi. and Faouzi Ghorbel.},
title={An Improved VGG16 Model Based on Complex Invariant Descriptors for Medical Images Classification},
booktitle={Proceedings of the 13th International Conference on Pattern Recognition Applications and Methods - ICPRAM},
year={2024},
pages={444-452},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012467800003654},
isbn={978-989-758-684-2},
issn={2184-4313},
}

TY - CONF

JO - Proceedings of the 13th International Conference on Pattern Recognition Applications and Methods - ICPRAM
TI - An Improved VGG16 Model Based on Complex Invariant Descriptors for Medical Images Classification
SN - 978-989-758-684-2
IS - 2184-4313
AU - Amine Mezghich, M.
AU - Hmida, D.
AU - Nahdi, T.
AU - Ghorbel, F.
PY - 2024
SP - 444
EP - 452
DO - 10.5220/0012467800003654
PB - SciTePress