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Analyze WZT Images to Predict the Type of Depression and Dementia in the Elderly Using Deep Learning
- Authors
- Kim, Kyung-Yeul; Yang, Young-Bo; Kim, Mi-Ra; Park, Ji Su; Kim, Jihie
- Issue Date
- Sep-2024
- Publisher
- Springer Science and Business Media Deutschland GmbH
- Keywords
- Convolution Neural Network; deep learning; depression and dementia; prediction; Wartegg-Zeichentest
- Citation
- Advances in Computer Science and Ubiquitous Computing, v.1190, pp 325 - 329
- Pages
- 5
- Indexed
- SCOPUS
- Volume
- 1190
- Start Page
- 325
- End Page
- 329
- ISSN
- 1876-1100
1876-1119
- Abstract
- Analyzing depression and dementia in the elderly using deep learning based on drawing images created by the elderly in the Wartegg-Zeichentest (WZT) is limited. This study utilized drawing data expressed through the WZT test and employed deep learning to predict depression and dementia in the elderly. The analysis of geriatric diseases using Deep Learning necessitates further information gathering and related research on diseases, with the expectation of creating numerous opportunities in various fields of deep learning. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.
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Related Researcher
- Kim, Ji Hie
- College of Advanced Convergence Engineering (Department of Computer Science and Artificial Intelligence)