Voxelwise Detection of Cerebral Microbleed in CADASIL Patients by Naive Bayesian Classifier

DOI

10.2991/icitme-18.2018.35How to use a DOI?

Keywords

CADASIL; voxel; naive Baysian classifier; cross validation

Abstract

It is important to detect cerebral microbleed voxels from the brain image of cerebral autosomal-dominant arteriopathy with subcortical infarcts and Leukoencephalopathy (CADASIL) patients. Methods developed by other researchers before have a high variablity of intra-observer and inter-observer. In our study, we collect our dataset from the 20 brain volumetric images, 10 for CADASIL patients and 10 for healthy controls. And we used naive baysian classifier to get the results. We use cross validation to improve the performance of naive Baysian classifier. The results show that the average sensitivity is 74.53±0.96%, the average specificity is 74.51±1.05%, and the average accuracy is 74.52±1.00%.

Copyright

© 2018, 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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TY  - CONF
AU  - Fangzhou Bao
AU  - Meiling Shi
AU  - Felix Macdonald
PY  - 2018/08
DA  - 2018/08
TI  - Voxelwise Detection of Cerebral Microbleed in CADASIL Patients by Naive Bayesian Classifier
BT  - Proceedings of the 2018 International Conference on Information Technology and Management Engineering (ICITME 2018)
PB  - Atlantis Press
SP  - 176
EP  - 180
SN  - 1951-6851
UR  - https://doi.org/10.2991/icitme-18.2018.35
DO  - 10.2991/icitme-18.2018.35
ID  - Bao2018/08
ER  -