Intelligent Database Systems Lab 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
Intelligent Database Systems Lab Outlines Motivation Objectives Methodology Experiments Conclusions Comments
Intelligent Database Systems Lab Motivation The Alzheimer’s disease is at an advanced stage and there is no a known cure for the AD disease since currently.
Intelligent Database Systems Lab Objectives In order to deal with objective diagnosis of the AD, this paper use many techniques to diagnosis more effective better than before.
Intelligent Database Systems Lab Methodology- ADNI DB(25Normal 、 25AD)
Intelligent Database Systems Lab Methodology -Segmentation Feature extraction CONN linkage for SOM clustering segmentation process : two stages 1. Classification 2. SOM Clustering
Intelligent Database Systems Lab Methodology
Intelligent Database Systems Lab Methodology Use LVQ3 algorithm: Length w=
Intelligent Database Systems Lab Methodology feature reduction : Feature generation, computed reduced features
Intelligent Database Systems Lab Methodology
Intelligent Database Systems Lab Methodology - SVM Function h: Radial Basis Function
Intelligent Database Systems Lab 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)
Intelligent Database Systems Lab Experiments – Classification results
Intelligent Database Systems Lab Experiments
Intelligent Database Systems Lab Experiments
Intelligent Database Systems Lab Conclusions –The results provided by the presented method outperform other previous approaches based on MRI images..
Intelligent Database Systems Lab Comments Advantages –Good classification Applications –Diagnosis Alzheimer’s disease