Research Article
Alzheimer’s Disease Diagnosis Based on Cortical and Subcortical Features
Table 12
Algorithm performance comparison over OASIS and ADNI MRI data.
| Approach | Year | Dataset | Classifier | Modalities | AD vs HC | ACC | SEN | SPEC |
| Cuingnet et al. [23] | 2011 | ADNI | SVM | MRI | NA | 81% | 95% | Cho et al. [25] | 2012 | ADNI | LDA | MRI | NA | 82% | 93% | Chyzhyk et al. [17] | 2012 | OASIS | Kernel-LICA-DC | MRI | 74.25 | 96 | 52.5 | Lama et al. [22] | 2017 | ADNI | RELM | MRI | 77.88 | 68.85 | 83.54 | Jha and Kwon [19] | 2017 | OASIS | Sparse autoencoder | MRI | 91.6 | 98.09 | 84.09 | Islam and Zhang [20] | 2017 | OASIS | Ensemble of deep convolutional neural networks | MRI | 93.18 | NA | 93 | Farhan et al. [21] | 2014 | OASIS | Ensemble of classifier | MRI | 93.75 | 100 | 87.5 | Khajehnejad et al. [18] | 2017 | OASIS | Semisupervised classifier | MRI | 93.86 | 94.65 | 93.22 | Jha et al. [16] | 2018 | ADNI | ELM | MRI | 90.26 | 90.27 | 90.20 | OASIS | 95.27 | 96.59 | 93.03 | Proposed method | 2018 | NRCD | Softmax classifier | MRI | 99.34 | 98.14 | 100 | 2018 | OASIS | Softmax classifier | MRI | 98.40 | 93.75 | 100 |
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