Cellular Automata Framework for Dementia Classification Using Explainable AI
The clinical dementia rating scale has been used for analyzing dementia severity based on cognitive impairments. Many researchers have introduced various statistical methods and machine learning techniques for the classification of dementia severity. Feature importance with deep learning architectur...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2024-07-01
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| Series: | Engineering Proceedings |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2673-4591/68/1/36 |
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| Summary: | The clinical dementia rating scale has been used for analyzing dementia severity based on cognitive impairments. Many researchers have introduced various statistical methods and machine learning techniques for the classification of dementia severity. Feature importance with deep learning architecture can give a better analysis of the dementia severity. A CA framework has been proposed for the classification of cognitive impairment, and LIME has been used for explaining the local interpretability. Feature vectors for healthy and unhealthy classes have been converted to redistributed CA images. These CA images have been classified using deep learning architecture, and promising results have been achieved. GRAD-CAM and LIME explainer have captured the feature importance of the cognitive impairment of CA images. |
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| ISSN: | 2673-4591 |