International Journal of Computational Intelligence Systems

Volume 7, Issue 6, December 2014, Pages 1123 - 1136

A new similarity measure based on Bayesian Network signature correspondence for brain tumors cases retrieval

Authors
Hedi Yazid, Karim Kalti, Najoua Essoukri Benamara
Corresponding Author
Hedi Yazid
Received 21 July 2013, Accepted 1 February 2014, Available Online 1 December 2014.
DOI
10.1080/18756891.2014.963980How to use a DOI?
Keywords
Medical case retrieval, Brain Tumors, Similarity Measure, Bayesian networks, Bayesian inference, graph signature
Abstract

Case retrieval constitutes an interesting area of research which contributes to the evolution of several domains. The similarity measure module is a fundamental step in the retrieval process which affects remarkably on a retrieval system. In this context, we suggest in this paper a similarity measure applied to brain tumor cases retrieval. The rationale behind the proposed measure consists in quantifying the diagnosis correspondence followed by a clinician while comparing two medical cases. Our idea is characterized by the use of the Bayesian inference in the formulation of the proposed measure. The Bayesian network is applied in the classification task and it describes the decision-making process of a radiologist facing a tumor. The proposed similarity algorithm is based essentially on graph correspondence based on signature nodes comparison from the Bayesian classifiers. experiments were directed to compare the performance of the proposed similarity measure method with classical methods of similarity quantification. The performance indices of our proposition are promising.

Copyright
© 2017, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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Journal
International Journal of Computational Intelligence Systems
Volume-Issue
7 - 6
Pages
1123 - 1136
Publication Date
2014/12/01
ISSN (Online)
1875-6883
ISSN (Print)
1875-6891
DOI
10.1080/18756891.2014.963980How to use a DOI?
Copyright
© 2017, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Cite this article

TY  - JOUR
AU  - Hedi Yazid
AU  - Karim Kalti
AU  - Najoua Essoukri Benamara
PY  - 2014
DA  - 2014/12/01
TI  - A new similarity measure based on Bayesian Network signature correspondence for brain tumors cases retrieval
JO  - International Journal of Computational Intelligence Systems
SP  - 1123
EP  - 1136
VL  - 7
IS  - 6
SN  - 1875-6883
UR  - https://doi.org/10.1080/18756891.2014.963980
DO  - 10.1080/18756891.2014.963980
ID  - Yazid2014
ER  -