Table 11.

Multiclass classification

Modality	Feature selection/dimensionality reduction	Classifier	N	Cross-validated	Classification challenge				Unique approach	Reference
MRI	Canonical correlation analysis + novel sparse multitask learning	SVM	202	Y	Multiclass classification				Uses canonical correlation analysis to determine correlations between features of different modalities. Applies to multiclass classification problem.	[441]
PET					AD versus MCI versus CN		AD versus MCIc versus MCInc versus NC
					ACC	71.9	ACC	61.9
MRI	Canonical correlation analysis + novel sparse multitask learning	SVM	202	Y	ACC	68.6	ACC	56.2	Similar to [441].	[442]
PET
MRI	Linear discriminant analysis + locality preserving projection	SVM	202	Y	ACC	76.4	ACC	61.1	Combines two subspace learning techniques, linear discriminant analysis and locality preserving projection for feature selection and applied to multiclass and binary classification problems.	[445]
PET
MRI	Deep architecture + multitask learning	SVM	202	Y	ACC	62.5	ACC	52.5	Uses novel deep learning architecture that discards uninformative features in a hierarchical manner during multitask learning.	[417]
FDG PET
CSF

Abbreviations: MRI, magnetic resonance imaging; PET, positron emission tomography; SVM, support vector machine; AD, Alzheimer’s disease; MCI, mild cognitive impairment; CN, cognitively normal; MCIc, mild cognitive impairment converters (progressive MCI); MCInc, mild cognitive impairment nonconverters (stable MCI); NC, normal cognition; ACC, accuracy; CSF, cerebrospinal fluid; FDG PET, ¹⁸F-flurodeoxyglucose positron emission tomography.