  This paper aims to develop a new deep learning-inspired gaming approach for
early detection of dementia. This research integrates a robust convolutional
neural network (CNN)-based model for early dementia detection using health
metrics data as well as facial image data through a cognitive assessment-based
gaming application. We have collected 1000 data samples of health metrics
dataset from Apollo Diagnostic Center Kolkata that is labeled as either
demented or non-demented for the training of MOD-1D-CNN for the game level 1
and another dataset of facial images containing 1800 facial data that are
labeled as either demented or non-demented is collected by our research team
for the training of MOD-2D-CNN model in-game level 2. In our work, the loss for
the proposed MOD-1D-CNN model is 0.2692 and the highest accuracy is 70.50% for
identifying the dementia traits using real-life health metrics data. Similarly,
the proposed MOD-2D-CNN model loss is 0.1755 and the highest accuracy is
obtained here 95.72% for recognizing the dementia status using real-life
face-based image data. Therefore, a rule-based weightage method is applied to
combine both the proposed methods to achieve the final decision. The MOD-1D-CNN
and MOD-2D-CNN models are more lightweight and computationally efficient
alternatives because they have a significantly lower number of parameters when
compared to the other state-of-the-art models. We have compared their
accuracies and parameters with the other state-of-the-art deep learning models.
