An Improved VGG16 Model Based on Complex Invariant Descriptors for Medical Images Classification

Mohamed Amine Mezghich, Dorsaf Hmida, Taha Nahdi, Faouzi Ghorbel

2024

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.

Paper Citation

in Harvard Style

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 - Volume 1: ICPRAM; ISBN 978-989-758-684-2, SciTePress, pages 444-452. DOI: 10.5220/0012467800003654

in Bibtex Style

@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 - Volume 1: ICPRAM},
year={2024},
pages={444-452},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012467800003654},
isbn={978-989-758-684-2},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 13th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM
TI - An Improved VGG16 Model Based on Complex Invariant Descriptors for Medical Images Classification
SN - 978-989-758-684-2
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