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.

通过整合基于卷积神经网络的健康指标数据和面部图像数据的认知评估游戏应用程序，本研究旨在开发一种新的深度学习启发的游戏方法，以早期检测痴呆症。通过我们的工作，我们的提出的 MOD-1D-CNN 模型可以使用真实生活的健康指标数据识别 70.50％的痴呆特征，而提出的 MOD-2D-CNN 模型则可以使用真实生活的面部图像数据识别 95.72％的痴呆状态。因此，我们应用一种基于规则的加权方法来结合这两种方法以达到最终决策。