Diagnosis of Alzheimer’s Disease Severity with fMRI Images Using Robust Multitask Feature Extraction Method and Convolutional Neural Network (CNN)

Table 1

Summary research on Alzheimer’s disease diagnosis methods.

Author	Year	Database	Modality	Method	Accuracy
Suk and Shen [32]	2013	Alzheimer’s Disease Neuroimaging Initiative (ADNI)	PET, MRI, CSF	Stacked autoencoder, SVM	95.9
Suk al.[33]	2014	ADNI	PET, MRI	Deep Boltzmann machine	95.4
Liu et al. [34]	2016	ADNI	MRI	Influence of subclass number, multiview feature extraction, subclass clustering-based feature selection, SVM	93.8
Zu et al. [35]	2016	ADNI	PET, MRI	Label-aligned multi-task feature selection, support vector machine	96.0
Sarraf and Tofighi [36]	2016	ADNI	fMRI	LeNet-5	96.85
Sarraf and Tofighi [37]	2016	ADNI	MRI, fMRI	LeNet, GoogleNet	98.84
Li et al. [38]	2017	ADNI	MRI	CNN	88.31
Amoroso et al. [39]	2018	ADNI	MRI	Random Forest, deep neural network, fuzzy logic	38.8
Liu et al. [40]	2018	ADNI	MRI, PET	2D and 3D CNN,	93.26
Yang et al. [41]	2018	ADNI	MRI	The convolutional neural network, 3DVGGNET, 3DRESNET	76.6
Wang et al. [42]	2018	Open Access Series of Imaging Studies	MRI	CNN	97.65
Khvostikov et al. [43]	2018	ADNI	MRI, DTI	CNN	96.7
Shi et al. [44]	2018	ADNI	MRI, PET	Multimodal stacked deep polynomial network, SVM	97.13
Ramzan et al. [45]	2019	ADNI	fMRI	Off-the-shelf and fine-tuned	97.88
Parmar et al. [46]	2020	ADNI	fMRI	3D CNN	96.55
Duc et al. [47]	2020	ADNI	fMRI	3D CNN and SVM-RFE	85.27
Li et al. [48]	2020	ADNI	4D fMRI	3D CNN and C3d-LSTM	89.47
Al-Khuzaie et al. [49]	2021	Alzheimer Network (AlzNet)	2D fMRI	CNN	99.30
Bhaskaran and Anandan [50]	2021	Research Anthology on Diagnosing and Treating Neurocognitive Disorders	rsfMRI	Graph metrics and lateralization	97.54
Luo et al. [51]	2021	Population-specific Chinese brain atlas	rsfMRI	Graph metrics and false discovery rate (FDR)	95.67
Ahmadi et al. [52]	2021	Harvard Medical School	MRI	Robust PCA and CNN method	96