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Authors: Stefan Schnürle 1 ; Marc Pouly 1 ; Tim vor der Brück 1 ; Alexander Navarini 2 and Thomas Koller 1

Affiliations: 1 Lucerne University of Applied Sciences and Arts, Switzerland ; 2 University Hospital Zurich, Switzerland

ISBN: 978-989-758-220-2

Keyword(s): Machine Learning, Support Vector Machines, Classification, Eczema Detection and Quantification.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Biomedical Engineering ; Biomedical Signal Processing ; Computational Intelligence ; Data Manipulation ; Evolutionary Computing ; Health Engineering and Technology Applications ; Human-Computer Interaction ; Industrial Applications of AI ; Knowledge Discovery and Information Retrieval ; Knowledge-Based Systems ; Machine Learning ; Methodologies and Methods ; Neurocomputing ; Neurotechnology, Electronics and Informatics ; Pattern Recognition ; Physiological Computing Systems ; Sensor Networks ; Soft Computing ; Symbolic Systems

Abstract: Hand eczema is one of the most frequent skin diseases affecting up to 14% of the population. Early detection and continuous observation of eczemas allows for efficient treatment and can therefore relieve symptoms. However, purely manual skin control is tedious and often error prone. Thus, an automatic approach that can assist the dermatologist with his work is desirable. Together with our industry partner swiss4ward, we devised an image processing method for hand eczema segmentation based on support vector machines and conducted several experiments with different feature sets. Our implementation is planned to be integrated into a clinical information system for operational use at University Hospital Zurich. Instead of focusing on a high accuracy like most existing state-of-the-art approaches, we selected F1 score as our primary measure. This decision had several implications regarding the design of our segmentation method, since all popular implementations of support vector machines a im for optimizing accuracy. Finally, we evaluated our system and achieved an F1 score of 58.6% for front sides of hands and 43.8% for back sides, which outperforms several state-of-the-art methods that were tested on our gold standard data set as well. (More)

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Paper citation in several formats:
Schnürle S., Pouly M., vor der Brück T., Navarini A. and Koller T. (2017). On using Support Vector Machines for the Detection and Quantification of Hand Eczema.In Proceedings of the 9th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART, ISBN 978-989-758-220-2, pages 75-84. DOI: 10.5220/0006125000750084

@conference{icaart17,
author={Stefan Schnürle and Marc Pouly and Tim vor der Brück and Alexander Navarini and Thomas Koller},
title={On using Support Vector Machines for the Detection and Quantification of Hand Eczema},
booktitle={Proceedings of the 9th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,},
year={2017},
pages={75-84},
publisher={ScitePress},
organization={INSTICC},
doi={10.5220/0006125000750084},
isbn={978-989-758-220-2},
}

TY - CONF

JO - Proceedings of the 9th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,
TI - On using Support Vector Machines for the Detection and Quantification of Hand Eczema
SN - 978-989-758-220-2
AU - Schnürle S.
AU - Pouly M.
AU - vor der Brück T.
AU - Navarini A.
AU - Koller T.
PY - 2017
SP - 75
EP - 84
DO - 10.5220/0006125000750084

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