Cerebral Microbleed Detection by Wavelet Entropy and Naive Bayes Classifier

被引：0
|
作者：
Wang, Hai-nan [1 ,2 ,3 ]
Gagnon, Beatrice [1 ,2 ,3 ]
机构：
[1] Nanjing Normal Univ, Sch Comp Sci & Technol, Nanjing 210023, Jiangsu, Peoples R China
[2] State Key Lab Digital Publishing Technol, Beijing 100871, Peoples R China
[3] Univ Toronto, Div Engn Sci, Toronto, ON M5S2E4, Canada
来源：
PROCEEDINGS OF THE 2ND INTERNATIONAL CONFERENCE ON BIOMEDICAL AND BIOLOGICAL ENGINEERING 2017 (BBE 2017) | 2017年 / 4卷
关键词：
Wavelet entropy; Cerebral microbleed; Naive Bayesian classifier; PATHOLOGICAL BRAIN DETECTION; SUPPORT VECTOR MACHINE; IMAGE CLASSIFICATION; TSALLIS ENTROPY; DETECTION SYSTEM; NEURAL-NETWORK; HYBRIDIZATION; TRANSFORM; ALGORITHM; MOMENT;
D O I：
暂无
中图分类号：
R914 [药物化学];
学科分类号：
100701 ;
摘要：
(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 76.90 +/- 1.81%, 76.91 +/- 1.58%, and 76.90 +/- 1.67%, respectively. Our method can identify the CMB areas using only 1.41 seconds. (Conclusion) Our method is effective and rapid.
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页码：505 / 510
页数：6
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