Proceedings of the 2nd International Conference on Biomedical and Biological Engineering 2017 (BBE 2017)

Cerebral Microbleed Detection by Wavelet Entropy and Naive Bayes Classifier

Authors
Hai-nan WANG, Beatrice Gagnon
Corresponding Author
Hai-nan WANG
Available Online May 2017.
DOI
https://doi.org/10.2991/bbe-17.2017.81How to use a DOI?
Keywords
Wavelet entropy, Cerebral microbleed, Naive Bayesian classifier.
Abstract
(Aim) Current cerebral microbleed detection methods are too complicated, and difficult to train. (Method) We enrolled 10 subjects diagnosed as cerebral microbleed.Our method combined wavelet entropy and naive Bayes classifier. (Results) The simulation results over 10 times of 10-fold cross validation showed that the average sensitivity, average specificity, and average accuracy of our method are 76.90ñ1.81%, 76.91ñ1.58%, and 76.90ñ1.67%, respectively. Our method can identify the CMB areas using only 1.41 seconds. (Conclusion) Our method is effective and rapid.
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This is an open access article distributed under the CC BY-NC license.

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Proceedings
2nd International Conference on Biomedical and Biological Engineering 2017 (BBE 2017)
Part of series
Advances in Biological Sciences Research
Publication Date
May 2017
ISBN
978-94-6252-362-3
ISSN
2468-5747
DOI
https://doi.org/10.2991/bbe-17.2017.81How to use a DOI?
Open Access
This is an open access article distributed under the CC BY-NC license.

Cite this article

TY  - CONF
AU  - Hai-nan WANG
AU  - Beatrice Gagnon
PY  - 2017/05
DA  - 2017/05
TI  - Cerebral Microbleed Detection by Wavelet Entropy and Naive Bayes Classifier
BT  - 2nd International Conference on Biomedical and Biological Engineering 2017 (BBE 2017)
PB  - Atlantis Press
SN  - 2468-5747
UR  - https://doi.org/10.2991/bbe-17.2017.81
DO  - https://doi.org/10.2991/bbe-17.2017.81
ID  - WANG2017/05
ER  -