TABLE III.

Functional MRI data used to train and validate both DeepAD and MCADNNet architectures. Improvement in the performance of classification is discovered by applying the decision-making algorithm in the post classification step. This improvement has been displayed from blue range (lower values) to white range (higher values). As mentioned above, the voting method enabled the pipeline to produce highly robust and reproducible outcomes.

	DeepAD – Functional MRI
Subject Level	Before Decision Making					After Decision Making
Experiment	1	2	3	4	5	1	2	3	4	5
AD_NC	0.97	0.9327	0.9468	0.9032	0.966	1	1	0.9706	0.9211	1
AD_NC_MCI	0.91	0.854	0.9068	0.8968	0.8893	0.9444	0.9444	0.9861	0.9583	0.9861
AD_MCI	0.92	0.9345	0.9683	0.9327	0.9336	0.9574	0.9787	1	0.9787	1
NC_MCI	0.92	0.9319	0.9199	0.9054	0.9223	0.9828	0.9655	0.9828	0.9828	0.9828
	MCADNNet - Functional MRI
Subject Level	Before Decision Making					After Decision Making
Experiment	1	2	3	4	5	1	2	3	4	5
AD_NC	0.95	0.9797	0.9711	0.8985	0.9663	0.9744	0.9744	1	0.9231	1
AD_NC_MCI	0.92	0.8614	0.8967	0.9097	0.8993	0.9861	0.9722	0.9722	0.9583	0.9861
AD_MCI	0.92	0.942	0.9666	0.9387	0.9484	0.9787	0.9787	1	0.9574	1
NC_MCI	0.92	0.9326	0.9196	0.9109	0.9294	0.9828	0.9828	0.9655	0.9828	0.9655