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Diagnosis of Depressive Disorder Model on Facial Expression Based on Fast R-CNN

This study examines related literature to propose a model based on artificial intelligence (AI), that can assist in the diagnosis of depressive disorder. Depressive disorder can be diagnosed through a self-report questionnaire, but it is necessary to check the mood and confirm the consistency of sub...

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Detalles Bibliográficos
Autores principales: Lee, Young-Shin, Park, Won-Hyung
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8871079/
https://www.ncbi.nlm.nih.gov/pubmed/35204407
http://dx.doi.org/10.3390/diagnostics12020317
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author Lee, Young-Shin
Park, Won-Hyung
author_facet Lee, Young-Shin
Park, Won-Hyung
author_sort Lee, Young-Shin
collection PubMed
description This study examines related literature to propose a model based on artificial intelligence (AI), that can assist in the diagnosis of depressive disorder. Depressive disorder can be diagnosed through a self-report questionnaire, but it is necessary to check the mood and confirm the consistency of subjective and objective descriptions. Smartphone-based assistance in diagnosing depressive disorders can quickly lead to their identification and provide data for intervention provision. Through fast region-based convolutional neural networks (R-CNN), a deep learning method that recognizes vector-based information, a model to assist in the diagnosis of depressive disorder can be devised by checking the position change of the eyes and lips, and guessing emotions based on accumulated photos of the participants who will repeatedly participate in the diagnosis of depressive disorder.
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spelling pubmed-88710792022-02-25 Diagnosis of Depressive Disorder Model on Facial Expression Based on Fast R-CNN Lee, Young-Shin Park, Won-Hyung Diagnostics (Basel) Article This study examines related literature to propose a model based on artificial intelligence (AI), that can assist in the diagnosis of depressive disorder. Depressive disorder can be diagnosed through a self-report questionnaire, but it is necessary to check the mood and confirm the consistency of subjective and objective descriptions. Smartphone-based assistance in diagnosing depressive disorders can quickly lead to their identification and provide data for intervention provision. Through fast region-based convolutional neural networks (R-CNN), a deep learning method that recognizes vector-based information, a model to assist in the diagnosis of depressive disorder can be devised by checking the position change of the eyes and lips, and guessing emotions based on accumulated photos of the participants who will repeatedly participate in the diagnosis of depressive disorder. MDPI 2022-01-27 /pmc/articles/PMC8871079/ /pubmed/35204407 http://dx.doi.org/10.3390/diagnostics12020317 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Lee, Young-Shin
Park, Won-Hyung
Diagnosis of Depressive Disorder Model on Facial Expression Based on Fast R-CNN
title Diagnosis of Depressive Disorder Model on Facial Expression Based on Fast R-CNN
title_full Diagnosis of Depressive Disorder Model on Facial Expression Based on Fast R-CNN
title_fullStr Diagnosis of Depressive Disorder Model on Facial Expression Based on Fast R-CNN
title_full_unstemmed Diagnosis of Depressive Disorder Model on Facial Expression Based on Fast R-CNN
title_short Diagnosis of Depressive Disorder Model on Facial Expression Based on Fast R-CNN
title_sort diagnosis of depressive disorder model on facial expression based on fast r-cnn
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8871079/
https://www.ncbi.nlm.nih.gov/pubmed/35204407
http://dx.doi.org/10.3390/diagnostics12020317
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