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Table 1 The mean and standard deviation of the classification accuracies, F1-scores, and AUCs over 5 different random training-validation-test data splittings. Our proposed model, which benefits from the incorporation of multiple complementary drawing tasks (clock drawing, cube-copying, and trail-making), self-attention mechanism, and soft labeling approach, achieved much higher mean accuracy, F1-score, and AUC than the baseline model

From: An explainable self-attention deep neural network for detecting mild cognitive impairment using multi-input digital drawing tasks

Models Accuracy F1-score AUC
VGG16 with only clock-drawing test 0.7478 ± 0.0071 0.3573 ± 0.0443 0.7429 ± 0.0131
VGG16 with only cube-copying test 0.7739 ± 0.0096 0.4994 ± 0.0477 0.7813 ± 0.0197
VGG16 with only trail-making test 0.7739 ± 0.0249 0.5283 ± 0.0548 0.7722 ± 0.0240
Multi-input VGG16 0.7986 ± 0.0071 0.5938 ± 0.0207 0.8115 ± 0.0192
Conv-Att with only clock-drawing test 0.7522 ± 0.0125 0.3586 ± 0.0309 0.7337 ± 0.0204
Conv-Att with only cube-copying test 0.7768 ± 0.0168 0.5095 ± 0.0515 0.7791 ± 0.0199
Conv-Att with only trail-making test 0.7696 ± 0.0167 0.5211 ± 0.0272 0.7662 ± 0.0231
Multi-input Conv-Att 0.7986 ± 0.0071 0.5981 ± 0.0221 0.8379 ± 0.0176
Multi-input Conv-Att with a soft label (proposed) 0.8116±0.0103 0.6539±0.0097 0.8375±0.0116