TABLE 2.
Method | Subjects
|
Methodology | Modalities | AD vs. NC (%) | MCI vs. NC (%) | ||
---|---|---|---|---|---|---|---|
AD | MCI | NC | |||||
Liu et al. [45] | 198 | N/A | 229 | Voxel GM+SVM Ensemble | MRI | 92.0 | N/A |
Cuingnet et al. [42] | 137 | N/A | 162 | Voxel Direct D+SVM | MRI | 88.58 | N/A |
Eskildsen et al. [41] | 194 | N/A | 226 | Cortical Thickness+SVM | MRI | 84.50 | N/A |
Duche. et al. [40] | 75 | N/A | 75 | Tensor-based Morphometry+SVM | MRI | 92.0 | N/A |
Min et al. [37] | 97 | N/A | 128 | Multi-Atlas ROI Features+SVM | MRI | 91.6 | N/A |
Gary et al. [44] | 37 | 75 | 35 | Random Forest | MRI+PET+CSF+Gen | 89.0 | 74.6 |
Tong et al. [43] | 35 | 75 | 77 | Graph Fusion | MRI+PET+CSF+Gen | 91.8 | 79.5 |
Liu et al. [39] | 85 | 169 | 77 | Deep Feature Learning | MRI+PET | 91.4 | 82.1 |
Ours | 93 | 202 | 101 | RFS-LDA | MRI+PET | 92.1 | 81.9 |