Bibliographic Details
| Title: |
Automatic diagnosis of depression based on attention mechanism and feature pyramid model. |
| Authors: |
Xu, Ningya1 (AUTHOR), Huo, Hua1,2 (AUTHOR) pacific_huo@126.com, Xu, Jiaxin1 (AUTHOR), Ma, Lan1 (AUTHOR), Wang, Jinxuan1 (AUTHOR) |
| Superior Title: |
PLoS ONE. 3/12/2024, Vol. 19 Issue 3, p1-19. 19p. |
| Subject Terms: |
*CONVOLUTIONAL neural networks, *STANDARD deviations, *DEPRESSED persons, *FACE, *PYRAMIDS, *MENTAL depression |
| Abstract: |
Currently, most diagnoses of depression are evaluated by medical professionals, with the results of these evaluations influenced by the subjective judgment of physicians. Physiological studies have shown that depressed patients display facial movements, head posture, and gaze direction disorders. To accurately diagnose the degree of depression of patients, this paper proposes a comprehensive framework, Cross-Channel Attentional Depression Detection Network, which can automatically diagnose the degree of depression of patients by inputting information from the facial images of depressed patients. Specifically, the comprehensive framework is composed of three main modules: (1) Face key point detection and cropping for video images based on Multi-Task Convolutional Neural Network. (2) The improved Feature Pyramid Networks model can fuse shallow features and deep features in video images and reduce the loss of miniscule features. (3) A proposed Cross-Channel Attention Convolutional Neural Network can enhance the interaction between tensor channel layers. Compared to other methods for automatic depression identification, a superior method was obtained by conducting extensive experiments on the depression dataset AVEC 2014, where the Root Mean Square Error and the Mean Absolute Error were 8.65 and 6.66, respectively. [ABSTRACT FROM AUTHOR] |
|
Copyright of PLoS ONE is the property of Public Library of Science and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) |
| Database: |
Academic Search Premier |
|
Full text is not displayed to guests. |
Login for full access.
|