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Table 2 Performance of the FCN models

From: Enhancing magnetic resonance imaging-driven Alzheimer’s disease classification performance using generative adversarial learning

(a)

1.5 T

Accuracy

Sensitivity

Specificity

F-1

MCC

ADNI test

0.8398 ± 0.0238

0.7363 ± 0.0514

0.9209 ± 0.0385

0.7972 ± 0.0325

0.6766 ± 0.0492

AIBL

0.8873 ± 0.0647

0.6309 ± 0.1393

0.9259 ± 0.0874

0.6126 ± 0.1016

0.5757 ± 0.1086

NACC

0.8157 ± 0.0224

0.6739 ± 0.0605

0.8989 ± 0.0616

0.7301 ± 0.0221

0.6032 ± 0.0425

(b)

3T*

Accuracy

Sensitivity

Specificity

F-1

MCC

ADNI test

0.8210 ± 0.0143

0.7411 ± 0.0312

0.8895 ± 0.0114

0.7923 ± 0.0195

0.6416 ± 0.0280

AIBL

0.9293 ± 0.0132

0.7143 ± 0.0000

0.9617 ± 0.0152

0.7276 ± 0.0365

0.6884 ± 0.0449

NACC

0.8429 ± 0.0069

0.7393 ± 0.0180

0.9037 ± 0.0134

0.7768 ± 0.0096

0.6585 ± 0.0149

  1. Accuracy, sensitivity, specificity, F1-score, and Matthew’s correlation coefficient are computed for the FCN models that used (a) 1.5-T scans and (b) 3-T* images, respectively
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Alzheimer's Research & Therapy

ISSN: 1758-9193