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Vision01:24

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Vision is the result of light being detected and transduced into neural signals by the retina of the eye. This information is then further analyzed and interpreted by the brain. First, light enters the front of the eye and is focused by the cornea and lens onto the retina—a thin sheet of neural tissue lining the back of the eye. Because of refraction through the convex lens of the eye, images are projected onto the retina upside-down and reversed.
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DNet: a depression recognition network combining residual network and vision transformer.

Zhongyi Jiang1, Ke Xu2, Xing Gao2

  • 1School of Computer and Artificial Intelligence, Changzhou University, Changzhou, 213164, Jiangsu, China. jzy@cczu.edu.cn.

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|September 30, 2025
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Summary

This study introduces DNet, an efficient network using facial images to identify depression by analyzing subtle expression changes. DNet achieves high accuracy in depression severity prediction, aiding diagnosis and treatment.

Keywords:
Deep LearningDepressionDnetFacial ImagesViT

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Psychiatry

Background:

  • Depression is a widespread mental health disorder with diagnostic and treatment challenges.
  • Facial expressions offer potential biomarkers for depression severity.
  • Existing methods struggle to capture subtle, localized facial changes indicative of depression.

Purpose of the Study:

  • To develop an efficient deep learning network (DNet) for accurate depression identification using facial images.
  • To leverage both global and local facial features for improved depression severity assessment.
  • To enhance depression diagnosis through advanced feature fusion and attention mechanisms.

Main Methods:

  • Proposed DNet architecture with a Feature Extraction Module (FEM) and Vision Transformer (ViT) Block.
  • FEM utilizes channel and positional attention for feature maps from global and local facial images.
  • Feature fusion via FPN and ViT Block for comprehensive semantic feature learning.

Main Results:

  • DNet achieved Mean Absolute Error (MAE) of 6.09 and Root Mean Square Error (RMSE) of 7.85 on the AVEC2014 dataset.
  • On the CZ2023 dataset, DNet obtained MAE of 6.73 and RMSE of 8.47.
  • Experimental results validate the effectiveness of DNet in depression identification.

Conclusions:

  • The DNet network demonstrates high accuracy in predicting depression severity from facial images.
  • The proposed method effectively fuses local and global facial features for enhanced identification.
  • DNet shows promise as a tool to assist in the diagnosis and management of depression.