Analyze WZT Images to Predict the Type of Depression and Dementia in the Elderly Using Deep Learning

Kyung Yeul Kim, Young Bo Yang, Mi Ra Kim, Ji Su Park, Jihie Kim

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

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.

Original languageEnglish
Title of host publicationAdvances in Computer Science and Ubiquitous Computing - Proceedings of CUTE/CSA 2023
EditorsJi Su Park, Laurence T. Yang, Yi Pan, James J. Park
PublisherSpringer Science and Business Media Deutschland GmbH
Pages325-329
Number of pages5
ISBN (Print)9789819724468
DOIs
StatePublished - 2024
Event15th International Conference on Computer Science and its Applications, CSA 2023 and 17th KIPS International Conference on Ubiquitous Information Technologies and Applications, CUTE 2023 - Nha Trang, Viet Nam
Duration: 18 Dec 202320 Dec 2023

Publication series

NameLecture Notes in Electrical Engineering
Volume1190 LNEE
ISSN (Print)1876-1100
ISSN (Electronic)1876-1119

Conference

Conference15th International Conference on Computer Science and its Applications, CSA 2023 and 17th KIPS International Conference on Ubiquitous Information Technologies and Applications, CUTE 2023
Country/TerritoryViet Nam
CityNha Trang
Period18/12/2320/12/23

Keywords

  • Convolution Neural Network
  • deep learning
  • depression and dementia
  • prediction
  • Wartegg-Zeichentest

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