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Recognizing schizophrenia using facial expressions based on convolutional neural network.

Xiaofei Zhang1, Tongxin Li2, Conghui Wang1

  • 1Department of Psychiatry, Tianjin Anding Hospital, Mental Health Center of Tianjin Medical University, Tianjin, China.

Brain and Behavior
|April 17, 2023
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Summary

Deep learning accurately identifies schizophrenia (SCZ) patients from facial expressions, achieving 95.18% accuracy. This technology reveals significant objective differences in facial expressions between SCZ patients and healthy individuals.

Keywords:
clinical cluesconvolutional neural networkfacial expressionsschizophrenia

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Area of Science:

  • Psychiatry
  • Computer Science
  • Medical Imaging

Background:

  • Facial expressions are key indicators of mental health conditions in psychiatry.
  • Objective analysis of facial expressions can aid in diagnosing mental health disorders.

Purpose of the Study:

  • To develop a deep learning model for recognizing schizophrenia (SCZ) patients using facial images.
  • To identify and analyze objective differences in facial expressions between SCZ patients and healthy controls.

Main Methods:

  • A convolutional neural network (CNN) was trained on facial expression videos from 106 SCZ patients and 101 healthy controls.
  • The CNN achieved classification accuracy and analyzed facial expressions for group differences using statistical methods.

Main Results:

  • The trained CNN accurately classified SCZ patients with 95.18% accuracy.
  • Statistically significant objective differences in facial expressions were identified between SCZ patients and healthy controls (p < .05).

Conclusions:

  • Deep learning algorithms show significant potential for identifying schizophrenia through facial expression analysis.
  • This approach could be integrated into mobile devices for early SCZ detection in clinical and daily settings.