Cerebral Microbleed Detection by Wavelet Entropy and Naive Bayes Classifier
Hai-nan WANG, Beatrice Gagnon
Available Online May 2017.
- https://doi.org/10.2991/bbe-17.2017.81How to use a DOI?
- Wavelet entropy, Cerebral microbleed, Naive Bayesian classifier.
- (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.
- 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 -