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Table 1.

Selected number of MRI from different classes for training and testing.

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Fig 1.

Pipeline for Alzheimer disease staging from structured magnetic resonance imaging (sMRI).

TVP means three-view patch; LH, RH, LI, RI, LA and RA are the acronyms for left hippocampus, right hippocampus, left insula, right insula, left amygdala and right amygdala, respectively.

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Fig 1 Expand

Table 2.

Performance of the trained classifiers.

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Table 2 Expand

Fig 2.

AD/NC, AD/mAD, AD/aAD, mAD/aAD, mAD/NC and aAD/NC classification performance based on test Three-View Patches (TVPs) generated from the hippocampal regions.

(A) Performance of CNN classifiers based on TVPs generated from the left hippocampus (LH). (B) Performance of CNN classifiers based on TVPs generated from the right hippocampus (RH).

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Fig 2 Expand

Fig 3.

AD/NC, AD/mAD, AD/aAD, mAD/aAD, mAD/NC and aAD/NC classification performance based on test Three-View Patches (TVPs) generated from amygdala regions.

(A)Performance of CNN classifiers based on TVPs generated from the left amygdala (LA). (B) Performance of CNN classifiers based on TVPs generated from the right amygdala (RA).

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Fig 3 Expand

Fig 4.

AD/NC, AD/mAD, AD/aAD, mAD/aAD, mAD/NC and aAD/NC classification performance based on testing Three-View Patches (TVPa) generated from insula regions.

(A) Performance of CNN classifiers based on TVPs generated from the left insula (LI). (B) Performance of CNN classifiers based on TVPs generated from the right insula (RI).

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Fig 4 Expand

Fig 5.

AD/NC, AD/mAD, AD/aAD, mAD/aAD, mAD/NC and aAD/NC classification performance based on six selected regions (hippocampi, amygdalae, insulae).

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Fig 5 Expand

Fig 6.

AD/NC, AD/mAD, AD/aAD, mAD/aAD, mAD/NC and aAD/NC classification accuracy based on test Three-View Patches (TVPs) generated from the ROIs.

Reported on GARD data.

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Fig 6 Expand

Fig 7.

AD/NC, AD/mAD,and mAD/NC classification accuracy based on testing Three-View Patches (TVP) generated from the regions of interests.

Reported on ADNI data.

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Fig 7 Expand

Table 3.

Comparison of the proposed approach with state-of-the-art approaches.

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Table 3 Expand

Table 4.

Performance of the trained classifiers.

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Table 4 Expand