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Automatic Identification of Depression Using Facial Images with Deep Convolutional Neural Network
BACKGROUND: Depression is a common disease worldwide, with about 280 million people having depression. The unique facial features of depression provide a basis for automatic recognition of depression with deep convolutional neural networks. MATERIAL/METHODS: In this study, we developed a depression...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
International Scientific Literature, Inc.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9281460/ https://www.ncbi.nlm.nih.gov/pubmed/35810326 http://dx.doi.org/10.12659/MSM.936409 |
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author | Kong, Xinru Yao, Yan Wang, Cuiying Wang, Yuangeng Teng, Jing Qi, Xianghua |
author_facet | Kong, Xinru Yao, Yan Wang, Cuiying Wang, Yuangeng Teng, Jing Qi, Xianghua |
author_sort | Kong, Xinru |
collection | PubMed |
description | BACKGROUND: Depression is a common disease worldwide, with about 280 million people having depression. The unique facial features of depression provide a basis for automatic recognition of depression with deep convolutional neural networks. MATERIAL/METHODS: In this study, we developed a depression recognition method based on facial images and a deep convolutional neural network. Based on 2-dimensional images, this method quantified the binary classification problem and distinguished patients with depression from healthy participants. Network training consisted of 2 steps: (1) 1020 pictures of depressed patients and 1100 pictures of healthy participants were used and divided into a training set, test set, and validation set at the ratio of 7: 2: 1; and (2) fully connected convolutional neural network (FCN), visual geometry group 11 (VGG11), visual geometry group 19 (VGG19), deep residual network 50 (ResNet50), and Inception version 3 convolutional neural network models were trained. RESULTS: The FCN model achieved an accuracy of 98.23% and a precision of 98.11%. The Vgg11 model achieved an accuracy of 94.40% and a precision of 96.15%. The Vgg19 model achieved an accuracy of 97.35% and a precision of 98.13%. The ResNet50 model achieved an accuracy of 94.99% and a precision of 98.03%. The Inception version 3 model achieved an accuracy of 97.10% and a precision of 96.20%. CONCLUSIONS: The results show that deep convolution neural networks can support the rapid, accurate, and automatic identification of depression. |
format | Online Article Text |
id | pubmed-9281460 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | International Scientific Literature, Inc. |
record_format | MEDLINE/PubMed |
spelling | pubmed-92814602022-07-22 Automatic Identification of Depression Using Facial Images with Deep Convolutional Neural Network Kong, Xinru Yao, Yan Wang, Cuiying Wang, Yuangeng Teng, Jing Qi, Xianghua Med Sci Monit Clinical Research BACKGROUND: Depression is a common disease worldwide, with about 280 million people having depression. The unique facial features of depression provide a basis for automatic recognition of depression with deep convolutional neural networks. MATERIAL/METHODS: In this study, we developed a depression recognition method based on facial images and a deep convolutional neural network. Based on 2-dimensional images, this method quantified the binary classification problem and distinguished patients with depression from healthy participants. Network training consisted of 2 steps: (1) 1020 pictures of depressed patients and 1100 pictures of healthy participants were used and divided into a training set, test set, and validation set at the ratio of 7: 2: 1; and (2) fully connected convolutional neural network (FCN), visual geometry group 11 (VGG11), visual geometry group 19 (VGG19), deep residual network 50 (ResNet50), and Inception version 3 convolutional neural network models were trained. RESULTS: The FCN model achieved an accuracy of 98.23% and a precision of 98.11%. The Vgg11 model achieved an accuracy of 94.40% and a precision of 96.15%. The Vgg19 model achieved an accuracy of 97.35% and a precision of 98.13%. The ResNet50 model achieved an accuracy of 94.99% and a precision of 98.03%. The Inception version 3 model achieved an accuracy of 97.10% and a precision of 96.20%. CONCLUSIONS: The results show that deep convolution neural networks can support the rapid, accurate, and automatic identification of depression. International Scientific Literature, Inc. 2022-07-10 /pmc/articles/PMC9281460/ /pubmed/35810326 http://dx.doi.org/10.12659/MSM.936409 Text en © Med Sci Monit, 2022 https://creativecommons.org/licenses/by-nc-nd/4.0/This work is licensed under Creative Common Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0 (https://creativecommons.org/licenses/by-nc-nd/4.0/) ) |
spellingShingle | Clinical Research Kong, Xinru Yao, Yan Wang, Cuiying Wang, Yuangeng Teng, Jing Qi, Xianghua Automatic Identification of Depression Using Facial Images with Deep Convolutional Neural Network |
title | Automatic Identification of Depression Using Facial Images with Deep Convolutional Neural Network |
title_full | Automatic Identification of Depression Using Facial Images with Deep Convolutional Neural Network |
title_fullStr | Automatic Identification of Depression Using Facial Images with Deep Convolutional Neural Network |
title_full_unstemmed | Automatic Identification of Depression Using Facial Images with Deep Convolutional Neural Network |
title_short | Automatic Identification of Depression Using Facial Images with Deep Convolutional Neural Network |
title_sort | automatic identification of depression using facial images with deep convolutional neural network |
topic | Clinical Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9281460/ https://www.ncbi.nlm.nih.gov/pubmed/35810326 http://dx.doi.org/10.12659/MSM.936409 |
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