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Table 2 Summary of DL-based studies for prediction and classification of AD from MRI

From: Application of deep learning in detecting neurological disorders from magnetic resonance images: a survey on the detection of Alzheimer’s disease, Parkinson’s disease and schizophrenia

Ref.

Reg.

DL Arch.

Pre-Proc.

Features

Dataset

Size

Accuracy

[33]

WB

SAE-3D, CNN

NM

CBF

ADNI

755 (AD, MCI, HC)

3-way 89.47%, AD vs. HC 95.39%, AD vs. MCI 86.84%, HC vs. MCI 92.11%

[31]

CNN

MC, STC, SS, HPF, SN, WMS, MD

SSIF

ADNI

52 AD\(^3\), 92 HC\(^3\), 211 AD\(^1\), 91 HC\(^1\)

99.9%\(^3\), \(^{5\alpha }\), 98.84%\(^1\), \(^{5\alpha }\)

[53]

CNN

MC, SST, HPF

SSIF

ADNI

28 AD, 15 NC

96.86%\(^{5\alpha }\)

[43]

CNN

SN, BC, MD

CBF

ADNI

33 AD, 22 LMCI, 49 MCI, 45 HC

98.88%

[42]

CNN

INUC, DC, NM

 

ADNI

193 AD, 151 HC

Class Score 95%\(^{5\alpha }\)

[69]

DNN

 

HPCV, CFV, LVV, ECT, MMSE

ADNI

60 AD, 60 HC, 60 cMCI 60 MCI

34.8%\(^{10\alpha }\)

[36]

3D-CNN

CR, TE, IRE, IN

3D CBF

ADNI

199 AD, 141 NC; 3D MRI AD 600 NC 598

98.74%

[56]

3D-CNN

SST, NM

CBF

ADNI

50 AD, 43 LMCI, 77 EMCI, 61 NC

 

[8]

PNN

IR, WF

GLCM, SED

ADNI

 

85%

[70]

VAE, MLP

SG

Shape feature

ADNI

150 NC, 90 AD, 160 EMCI, 160 LMCI

NC-AD 84%, NC-EMCI 56%, NC-LMCI 59%. AD-EMCI 81%, AD-LMCI 57%, EMCI-LMCI 63%

[60]

DBN

VBM

VV 3611, MSD 24

OASIS

49 AD, 49 HC

MSD 0.7360\(^{10\alpha }\), VV 0.9176\(^{10\alpha }\)

[47]

CNN

BE, MC, STC, IM, SS, THPF, NM, SN

CBF

ADNI

25 CN, 25 SMC, 25 EMCI, 25 LMCI, 13 MCI, 25 AD

CN 100%, SMC 96.85%, EMCI 97.38%, LMCI 97.43%, MCI 97.40%, AD 98.01%

[39]

BL

3D-CNN

NNM, BE, IR

4D features, clinical features

ADNI

192 AD, 184 HC, 181 pMCI, 228 sMCI

86%\(^{5\alpha }\)

[38]

HPC

CNN

IR, SG

HPC shape, texture, CBF

ADNI-1, ADNI- GO&2, AIBL

ADNI: 1711, AIBL: 435

 

[71]

LSTM-RNN

 

LSTM-based features

ADNI-1, ADNI-GO&2

822 MCI

 

[40]

CSA

SAE-DNN

MC, NUC, IN, SST, VL

310 Vol., CorTh, SAF, 5000 FDCM

ADNI, CAD- Dementia

171 CN, 232 MCI, 101 AD

Model-1 ADNI 56.6%\(^{10\alpha }\), CAD-Dementia 51.4%\(^{10\alpha }\) Model-2 ADNI 58%\(^{10\alpha }\), CAD-Dementia 56.8%\(^{10\alpha }\)

[41]

MCS

CNN

INUC

CBF

ADNI

47 AD 34 NC

 

[72]

SCS

CNN

 

CBF

OASIS

100 AD, 100 HC

VGG16: 92.3%\(^{5\alpha }\), Inception-V4: 96.25%\(^{5\alpha }\)

[37]

CNN

NM, IR, MD

CBF

ADNI, Milan

ADNI: 294 PAD, 763 MCI, 352 HC Milan: 124 PAD, 50 MCI, 55 HC

ADNI: 99%\(^{10\alpha }\), MILAN: 98%\(^{10\alpha }\)

[73]

CNN

 

CBF, 64

OASIS

416

80.25%

[46]

VB

CNN

SST, DA, CE, F

CBF

OASIS, MIRIAD

OASIS: 30 AD, 70 MCI, 316 HC MIRIAD: 46 MCI, 23 HC

0.8

  1. Ref reference, Reg region, DL Arch deep learning architecture, Pre-Proc pre-processing technique used in the study, WB whole brain, BL-brain lobes HPC–hippocampus, CSA cortical surface area, MCS middle cross section, SCS single cross section, SSIF shift and scale-invariant features; Vol.-volume; CorTh-cortical thickness; SAF-surface area features; HPCV-hippocampal volumes; CFV-cerebrospinal fluid volume; LVV-lateral ventricle volume; ECT-entorhinal cortex thickness; MMSE-baseline scores of Mini-Mental State examination; \(n\alpha\)-n-fold cross-validation, 4DF 4D features, CF clinical features, GLCM gray-level co-occurrence matrix, SED Sobel edge detector, MSD-maximal self-dissimilarity, VV voxel values