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Table 2 Use of SVM classifier for AD diagnosis using different multimodal imaging scans

From: Denouements of machine learning and multimodal diagnostic classification of Alzheimer’s disease

Technique

Classifier used

Modality

Accuracy

References

Pattern recognition

Convolutional neural network and sparse autoencoder

MRI and PET

AD vs NC: 3 × 3 × 3 (90.30%), 5 × 5 × 5 (91.10%), 7 × 7 × 7 (89.80%)

MCI vs NC: 3 × 3 × 3 (87.90%), 5 × 5 × 5 (89.10%), 7 × 7 × 7 (89.20%)

[95]

Pattern recognition

Random forest algorithm

MRI, PET, CSF, and APOE

AD vs NC (91.80%)

AD vs MCI vs NC (60.20%)

MCI vs NC (79.50%)

[96]

Pattern recognition

Multi kernel learning

sMRI and DTI

AD vs NC (90.20%)

MCI vs NC (79.42%)

AD vs MCI (76.63%)

[97]

Whole-brain parcellation

SVM

MRI and DTI

Multimodal with 73 features (72.40%)

Multimodal with 15 univariate features (72.11%)

Multimodal with 15 multivariate features (99.60%)

[98]

Multi-task feature selection

Multi KSVM

MRI and PET

AD vs NC (94.37%)

MCI vs NC (78.80%)

[99]