Background
Alzheimer’s Disease (AD)
Functional brain imaging
Computer Aided Diagnosis (CAD)
Methods
Subjects and preprocessing
SPECT database
(a)Demographic details of the SPECT dataset | |||
---|---|---|---|
Num. of Samples | Sex (M/F) (%) | Age μ [range/σ] | |
CTRL | 41 | 32.95/12.19 | 71.51 [46-85/7.99] |
AD 1 | 30 | 10.97/18.29 | 65.20 [23-81/13.36] |
AD 2 | 22 | 13.41/9.76 | 65.73 [46-86/8.25] |
AD 3 | 4 | 0/2.43 | 76 [69-83/9.90] |
(b)Demographic details of the PET dataset
| |||
Num. of Samples | Sex (M/F) (%) | Age μ [range/σ] | |
CTRL | 75 | 29.33/20.67 | 75.97 [62-86/4.91] |
AD | 75 | 31.33/18.67 | 75.72 [55-88/7.40] |
PET database
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Normal control subjects: Mini Mental State Examination (MMSE) scores between 24−30 (inclusive), a Clinical Dementia Ratio (CDR) of 0, non depressed, non MCI, and non demented. The age range of normal subjects will be roughly matched to that of MCI and AD subjects. Therefore, there should be minimal enrolment of normals under the age of 70.
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Mild AD: MMSE scores between 20−26 (inclusive), CDR of 0.5 or 1.0, and meets NINCDS/ADRDA criteria for probable AD.
Feature extraction
Principal Component Analysis: PCA
Partial Least Squares (PLS)
Large Margin Nearest Neighbors (LMNN)
Loss function
LMNN-RECT as feature reduction technique
Kernel LMNN
Feature/model selection
Classification
Large margin nearest classifier
Support vector machines classifier
Results and discussion
Experiments with SPECT database
SVM-linear classifier | Accuracy (%) | Sensitivity (%) | Specificity(%) |
---|---|---|---|
VAF | 83.51 | 83.93 | 82.93 |
PCA | 86.56 | 91.07 | 80.49 |
GMM | 89.69 | 90.24 | 89.29 |
Gaussian kernel PCA+LMNN Transformation
|
91.75
|
91.07
|
92.68
|
Gaussian kernel PLS+LMNN Transformation
|
90.72
|
91.07
|
90.24
|
PLS+LMNN Transformation
|
92.78
|
91.07
|
95.12
|
LMNN-RECT
|
80.28
|
70
|
87.80
|
LMNN-Classifier Accuracy (%) |
Euclidean
|
Mahalanobis
|
Energy
|
PCA
|
80.54
|
81.63
|
87.65
|
PLS
|
84.33
|
89.56
|
88.67
|