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Authors: Wiem Abbes and Dorra Sellami

Affiliation: CEM Laboratory, National Engineering School of Sfax, Sfax University, Soukra Street, Sfax 3038 and Tunisia

ISBN: 978-989-758-354-4

Keyword(s): Melanoma, Bag of Words, CAD System, Feature Extraction, Fuzzy C-Means, Deep Neural Network Classifier.

Related Ontology Subjects/Areas/Topics: Applications and Services ; Computer Vision, Visualization and Computer Graphics ; Device Calibration, Characterization and Modeling ; Features Extraction ; Image and Video Analysis ; Image Formation and Preprocessing ; Medical Image Applications

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 accura cy and sensitivity. (More)

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Paper citation in several formats:
Abbes, Wiem 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 - Volume 4 VISAPP: VISAPP, ISBN 978-989-758-354-4, pages 47-56. DOI: 10.5220/0007697900470056

@conference{visapp19,
author={Abbes, Wiem 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 - Volume 4 VISAPP: VISAPP,},
year={2019},
pages={47-56},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0007697900470056},
isbn={978-989-758-354-4},
}

TY - CONF

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

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