Deep Neural Network for Fuzzy Automatic Melanoma Diagnosis

Wiem Abbes, Dorra Sellami

2019

Abstract

Melanoma is the most serious type of skin cancer. We consider in this paper diagnosing melanoma based on skin lesion images obtained by common optical cameras. Given the lower quality of such images, we should cope with the imprecision of image data. This paper proposes a CAD system for decision making about the skin lesion severity. We first define the fuzzy modeling of the Bag-of-Words (BoW) of the lesion. Indeed, features are extracted from the skin lesion image related to four criteria inspired by the ABCD rule (Asymmetry, Border, Color, and Differential structures). Based on Fuzzy C-Means (FCM), membership degrees are determined for each BoW. Then, a deep neural network classifier is used for decision making. Based on a public database of 206 lesion images, experimental results demonstrate that the fuzzification of feature modeling presents good results in term of sensitivity (90.1%) and of accuracy (87.5%). A comparative study illustrates that our approach offers the best accuracy and sensitivity.

Paper Citation

in Harvard Style

Abbes W. and Sellami D. (2019). Deep Neural Network for Fuzzy Automatic Melanoma Diagnosis. In Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2019) - Volume 4: VISAPP; ISBN 978-989-758-354-4, SciTePress, pages 47-56. DOI: 10.5220/0007697900470056

in Bibtex Style

@conference{visapp19,
author={Wiem Abbes and Dorra Sellami},
title={Deep Neural Network for Fuzzy Automatic Melanoma Diagnosis},
booktitle={Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2019) - Volume 4: VISAPP},
year={2019},
pages={47-56},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0007697900470056},
isbn={978-989-758-354-4},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2019) - Volume 4: VISAPP
TI - Deep Neural Network for Fuzzy Automatic Melanoma Diagnosis
SN - 978-989-758-354-4
AU - Abbes W.
AU - Sellami D.
PY - 2019
SP - 47
EP - 56
DO - 10.5220/0007697900470056
PB - SciTePress