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Table 3 Classification performances of various deep neural network architectures on Mini Mental Status Exam (MMSE) and Korean Longitudinal Study on Cognitive Aging and Dementia Neuropsychological Battery (KLOSCAD-N) indicated by the area under the receiver operator curve (AUC) via five-cross validation on train dataset

From: Deep learning based low-cost high-accuracy diagnostic framework for dementia using comprehensive neuropsychological assessment profiles

   2D-CNN 2D-CNN Naïve 2D-CNN w/o SC 1D-CNN 1D-CNN w/o SC FCN FCN w/o SC NasNet
MMSEa mean - - - - - 0.9702 0.9583 -
  std - - - - - 0.0144 0.0139 -
KLOSCAD-N mean 0.9863 0.9850 0.9782 0.9848 0.9805 0.9830 0.9771 0.9813
  std 0.0048 0.0058 0.0057 0.0053 0.0042 0.0060 0.0070 0.0046
  1. aSince MMSE is composed with only five dimension (four demographic variables and one MMSE total-score, the other architecture are not applicable except FCN