Table 4.

Summery of DL-based studies for prediction and classification of SZ from MRI

Ref.	Regions	DL	Pre-Proc.	Feature (count)	Dataset	Size	Accuracy
[48]	VFN, CN, DMN	3D-CNN	MC, DN, STC, SS, TF, HPF	3D-ICA (15)	COBRE	72 SZs,74 HCs	98.09%${}^{10\alpha }$
[54]	AUD, DMN	2D-CNN	MC, SN, SS	ICA(13)	Self	42 SZs,40 HCs	slice-level DMN-72.65%${}^{5\alpha }$, AUD-78.34%${}^{5\alpha }$, subject-level DMN-91.32%${}^{5\alpha }$, AUD-98.75%${}^{5\alpha }$
[78]	WB	DNN	ICA	FNC, SBM (10)	MRN	69 SZs, 75 HCs	94.4%
[79]	WB	DNN		ROI (116)	OpenfMRI	50 SZs, 49 BD, 122 HCs	76.6%${}^{\alpha }$
[34]	WB	RNN	MC, DN, SF, TF, NM, LRg	SPF	FBIRN phase-II	87 SZs, 85 HCs	64%${}^{10\alpha }$
[52]	WB	DNN	STC, SN, SS	FNC (116)	COBRE	72 SZs,74 HCs	95.4%${}^{5\alpha }$
[35]	WB	DNN		FNC,SBM (410)	MLSP
[32]	WB	DBN	LR, ZN	NMF	Multisite	143 SZs,83 HCs	73.6%${}^{3\alpha }$
[50]	WB	SAE	STC, MC,SN, SM, F	VTS	COBRE	72 SZs,74 HCs	92%${}^{10\alpha }$
[51]	Atlas	FFBPNN	STC, MC, TF, NM, SS	FNC (20)	Hospital	39 SZs,31 HCs	79.3%${}^{10\alpha }$
[55]	WB	DNN, LRP	MC, SN	FNC, ICA (1225)	Multisite	558 SZs, 542 HCs	84.75%${}^{10\alpha }$
[49]	Cor., Str., Cere.	DNN	MC, NM, STC, SS, LD, TF	FNC (116)	Multisite	474 SZs,607 HCs	$\approx$83%${}^{10\alpha }$
[44]	Vent.	DBN	SST, BC, SG	SV, ROI	COBRE	72 SZs,76 HCs	ROI-83.3%${}^{3\alpha }$, SV-90%${}^{3\alpha }$
[80]	WB	MLP		ICA, RV	FBIRN	135 SZs,169 HCs	AUC- 0.85${}^{8\alpha }$, SD-0.05
[59]	WB	MLP	NM, SG, SS		Multisite	198 SZs,191 HCs	AUC-0.75${}^{10\alpha }$, SD-0.04

WB whole brain, Cor. cortical, Str. striatal, Cere cerebellar, Vent. ventricle, MRN mild research network, VFN visual frontal network, AUD auditory cortex, CN cerebellar network, DMN default mode network, $n\alpha =$n-fold cross-validation, SPF spatial feature, NMF neuro-morphometric features, VTS voxel time series, SV segmented ventricle, Self self-generated dataset