Detailed Information
Cited 0 time in 
Cited 0 time in 
Cited 0 time in
Metadata Downloads
Analyze WZT Images to Predict the Type of Depression and Dementia in the Elderly Using Deep Learning
Full metadata record
| DC Field | Value | Language |
|---|---|---|
| dc.contributor.author | Kim, Kyung-Yeul | - |
| dc.contributor.author | Yang, Young-Bo | - |
| dc.contributor.author | Kim, Mi-Ra | - |
| dc.contributor.author | Park, Ji Su | - |
| dc.contributor.author | Kim, Jihie | - |
| dc.date.accessioned | 2024-11-11T08:00:12Z | - |
| dc.date.available | 2024-11-11T08:00:12Z | - |
| dc.date.issued | 2024-09 | - |
| dc.identifier.issn | 1876-1100 | - |
| dc.identifier.issn | 1876-1119 | - |
| dc.identifier.uri | https://scholarworks.dongguk.edu/handle/sw.dongguk/56186 | - |
| dc.description.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. | - |
| dc.format.extent | 5 | - |
| dc.language | 영어 | - |
| dc.language.iso | ENG | - |
| dc.publisher | Springer Science and Business Media Deutschland GmbH | - |
| dc.title | Analyze WZT Images to Predict the Type of Depression and Dementia in the Elderly Using Deep Learning | - |
| dc.type | Article | - |
| dc.publisher.location | 독일 | - |
| dc.identifier.doi | 10.1007/978-981-97-2447-5_50 | - |
| dc.identifier.scopusid | 2-s2.0-85206128854 | - |
| dc.identifier.bibliographicCitation | Advances in Computer Science and Ubiquitous Computing, v.1190, pp 325 - 329 | - |
| dc.citation.title | Advances in Computer Science and Ubiquitous Computing | - |
| dc.citation.volume | 1190 | - |
| dc.citation.startPage | 325 | - |
| dc.citation.endPage | 329 | - |
| dc.type.docType | Conference Paper | - |
| dc.description.isOpenAccess | N | - |
| dc.description.journalRegisteredClass | scopus | - |
| dc.subject.keywordAuthor | Convolution Neural Network | - |
| dc.subject.keywordAuthor | deep learning | - |
| dc.subject.keywordAuthor | depression and dementia | - |
| dc.subject.keywordAuthor | prediction | - |
| dc.subject.keywordAuthor | Wartegg-Zeichentest | - |
- Files in This Item
- There are no files associated with this item.
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.
Related Researcher
- Kim, Ji Hie
- College of Advanced Convergence Engineering (Department of Computer Science and Artificial Intelligence)