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Updated: Jun 19, 2025

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A Hybrid Learning-Architecture for Mental Disorder Detection Using Emotion Recognition.

Joseph Aina¹, Oluwatunmise Akinniyi¹, Md Mahmudur Rahman²

¹Electrical and Computer Engineering Department, School of Engineering, Morgan State University, Baltimore, MD 21251, USA.

IEEE Access : Practical Innovations, Open Solutions

|July 26, 2024

Summary

This summary is machine-generated.

This study introduces a novel pipeline for mental disorder detection using facial expressions, achieving 81% accuracy with an ensemble model. This system aids in early diagnosis, potentially preventing severe outcomes of mental illness.

Keywords:

Object detection YOLOv8 feature fusion saliency maps

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Area of Science:

Computer Science
Artificial Intelligence
Psychiatry

Background:

Mental illness is a global health concern requiring timely detection and diagnosis.
Late diagnosis of mental disorders can lead to severe consequences, including death.
Traditional diagnostic methods can be enhanced with advanced technological approaches.

Purpose of the Study:

To develop a novel pipeline for analyzing facial expressions to detect mental disorders.
To create a system for generating a mental disorder dataset and predicting disorders from facial cues.
To improve the accuracy and interpretability of mental disorder diagnosis through AI.

Main Methods:

Utilized AffectNet and 2013 Facial Emotion Recognition (FER) datasets.
Developed a hybrid architecture using YOLOv8 for detecting mental disorder-associated visual cues.
Implemented an ensemble classifier fusing Convolutional Neural Networks (CNNs) and Visual Transformer (ViT) models.
Integrated Gradient-weighted Class Activation Mapping (Grad-CAM) and saliency maps for interpretability.

Main Results:

Achieved an overall accuracy of approximately 81% in predicting mental illness.
Successfully detected and classified visual cues linked to specific mental disorders.
Provided interpretable insights into the features influencing diagnostic predictions.

Conclusions:

The proposed AI pipeline offers a promising approach for early and accurate mental disorder detection.
The ensemble model enhances diagnostic accuracy and provides transparency for healthcare professionals.
Facial expression analysis holds significant potential for augmenting traditional mental health diagnostics.