Skin Cancer Classification using Deep Learning Models

Marwa Kahia, Amira Echtioui, Fathi Kallel, Ahmed Ben Hamida

2022

Abstract

In recent years, researches proved that Melanoma is the deadliest form of skin cancer. In the early stages, it can be treated successfully with surgery alone and survival rates are high. A large number of methods for Melanoma classification has been proposed to deal with this problem, but although they did not find better ways to create the final solution. Thus, our aim is to go further and explore the classic models in order to handle the Melanoma classification problem based on modified VGG16 and modified InceptionV3. The conducted experiments revealed the effectiveness of our proposed method based on modified VGG16 with 73.33% of accuracy, when compared to other state-of-the-art methods on the same data sets, in terms of finding optimal and effective solutions and improving the objective function.

Paper Citation

in Harvard Style

Kahia M., Echtioui A., Kallel F. and Ben Hamida A. (2022). Skin Cancer Classification using Deep Learning Models. In Proceedings of the 14th International Conference on Agents and Artificial Intelligence - Volume 1: SDMIS, ISBN 978-989-758-547-0, pages 554-559. DOI: 10.5220/0010976400003116

in Bibtex Style

@conference{sdmis22,
author={Marwa Kahia and Amira Echtioui and Fathi Kallel and Ahmed Ben Hamida},
title={Skin Cancer Classification using Deep Learning Models},
booktitle={Proceedings of the 14th International Conference on Agents and Artificial Intelligence - Volume 1: SDMIS,},
year={2022},
pages={554-559},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010976400003116},
isbn={978-989-758-547-0},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 14th International Conference on Agents and Artificial Intelligence - Volume 1: SDMIS,
TI - Skin Cancer Classification using Deep Learning Models
SN - 978-989-758-547-0
AU - Kahia M.
AU - Echtioui A.
AU - Kallel F.
AU - Ben Hamida A.
PY - 2022
SP - 554
EP - 559
DO - 10.5220/0010976400003116