Gamified AI Approch for Early Detection of Dementia
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866915082732044288 |
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| author | Maji, Paramita Kundu Acharya, Soubhik Paul, Priti Chakraborty, Sanjay Basu, Saikat |
| author_facet | Maji, Paramita Kundu Acharya, Soubhik Paul, Priti Chakraborty, Sanjay Basu, Saikat |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_16538 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Gamified AI Approch for Early Detection of Dementia Maji, Paramita Kundu Acharya, Soubhik Paul, Priti Chakraborty, Sanjay Basu, Saikat Computer Vision and Pattern Recognition Artificial Intelligence 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. |
| title | Gamified AI Approch for Early Detection of Dementia |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence |
| url | https://arxiv.org/abs/2405.16538 |