Table 4.

FFNN performance according to the combined features between CNN models and handcrafted features.

Models	Classes of AD	Accuracy %	Sensitivity %	AUC %	Precision %	Specificity %
FFNN with features of GoogleNet and handcrafted	Mild_Demented	98.9	99.4	99.12	97.8	99.52
	Moderate_Demented	100	99.56	98.52	100	99.68
	Non_Demented	99.8	99.87	99.56	99.7	99.86
	Very_Mild_Demented	99.1	98.72	99.46	99.8	99.72
	average ratio	99.50	99.39	99.17	99.33	99.70
FFNN with features of DenseNet-121 and handcrafted	Mild_Demented	99.4	99.3	99.63	98.9	99.8
	Moderate_Demented	100	99.98	99.84	100	99.55
	Non_Demented	99.7	99.58	99.28	99.8	99.71
	Very_Mild_Demented	99.8	99.68	99.49	99.8	99.6
	average ratio	999.70	99.64	99.56	99.63	99.67