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. Author manuscript; available in PMC: 2019 Feb 1.
Published in final edited form as: IEEE Trans Pattern Anal Mach Intell. 2018 Jan 17;41(2):515–522. doi: 10.1109/TPAMI.2018.2794470

TABLE 2.

Comparisons of the proposed method with state-of-the-art methods for diagnosis of AD and MCI. N/A: indicates that the methods did not report results for that experiment; CSF: cerebrospinal fluid; Gen: categorical genetic information.

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