lvq-svm based cad tool applied to structural mri for the diagnosis of the alzheimer’s disease
DESCRIPTION
LVQ-SVM based CAD tool applied to structural MRI for the diagnosis of the Alzheimer’s disease. Presenter : CHANG, SHIH-JIE Authors : Andrés Ortiz , Juan M. Górriz , Javier Ramírez , F.J. Martínez -Murcia 2013.PRL. Outlines. Motivation Objectives - PowerPoint PPT PresentationTRANSCRIPT
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Presenter : CHANG, SHIH-JIE
Authors : Andrés Ortiz , Juan M. Górriz , Javier Ramírez ,
F.J. Martínez-Murcia
2013.PRL
LVQ-SVM based CAD tool applied to structural MRI for the diagnosis of the Alzheimer’s disease
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Outlines
MotivationObjectivesMethodologyExperimentsConclusionsComments
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Motivation
The Alzheimer’s disease is at an advanced stage and there is no a known cure for the AD disease since currently .
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Objectives
• In order to deal with objective diagnosis of the AD, this paper use many techniques to diagnosis more effective better than before.
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Methodology- ADNI DB(25Normal、 25AD)
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Methodology -Segmentation
Feature extraction
CONN linkage for SOM clustering
segmentation process : two stages 1. Classification 2. SOM Clustering
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Methodology
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Methodology
Use LVQ3 algorithm:
Length w=
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Methodology
feature reduction :
Feature generation, computed reduced features
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Methodology
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Methodology - SVM
Function h:
Radial Basis Function
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Experiments 檢測為正確 檢測為錯誤
Disease (+) 生病
number of True Positives
(TP)
number of False Negatives
(FN)
Disease (-) 健康
number of False Positives
(FP)
number of True Negatives
(TN)
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Experiments – Classification results
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Experiments
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Experiments
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Conclusions– The results provided by the presented method
outperform other previous approaches based on MRI images. .
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Comments• Advantages
– Good classification• Applications
– Diagnosis Alzheimer’s disease