Table 4.

Comparison of results (%) obtained with the prior state-of-art methods developed for automated Alzheimer’s using MRI datasets

Author(s)	Number of images	Method	Validation	Results (%)
Puspaningrum et al. [47]	6400	Oversampling data, custom-designed CNN	Unspecified	Acc. = 55.27
Acharya et al. [48]	6400	Deep learning (VGG16, ResNet-50, and AlexNet)	Hold-out 75:25	Acc. = 95.70 Pre. = 91.90 Rec. = 92.30 F1 = 94.70
Fu’adah et al. [49]	6400	Deep learning (AlexNet)	Hold-out 75:25	Acc. = 94.58 Pre. = 92.0 Rec. = 90.20 F1 = 91.0
Subramoniam [50]	6400	Data augmentation, deep learning (ResNet101)	Hold-out 80:20	Acc. = 99.71 Pre. = 99.5 Rec. = 99.25 F1 = 99.5
Liang and Gu [51]	6400	Weakly supervised learning-based deep learning (ADGNET)	5-fold cross-validation	Acc. = 99.61 Pre. = 99.53 Spe. = 99.53 Rec. = 99.69
Alshammari and Mezher [52]	6400	Custom-designed CNN	Hold-out 80:20	Acc. = 97.0
Murugan et al. [53]	6400	Custom-designed CNN (DEMNET)	Hold-out 80:10:10	Acc. = 95.23 Auc. = 97.0 Coh.Ka. = 93.0
Our method	6400	PFP-HOG, IChi2, kNN	10-fold cross-validation	Acc. = 100 Pre. = 100 Rec. = 100 F1 = 100

Acc. Accuracy, Pre. Precision, Rec. recall, F1 F1-score, Spe. Specificity, Coh.Ka. Cohen’s kappa, Auc. area under curve