Facial image analysis for automated suicide risk detection with deep neural networks

Accurately assessing suicide risk is a critical concern in mental health care. Traditional methods, which often rely on self-reporting and clinical interviews, are limited by their subjective nature and may overlook non-verbal cues. This study introduces an innovative approach to suicide risk assess...

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Bibliographic Details
Published in:The Artificial intelligence review 2024-09, Vol.57 (10), p.274, Article 274
Main Authors: Rashed, Amr E. Eldin, Atwa, Ahmed E. Mansour, Ahmed, Ali, Badawy, Mahmoud, Elhosseini, Mostafa A., Bahgat, Waleed M.
Format: Article
Language:English
Subjects:
Accuracy
Artificial Intelligence
Artificial neural networks
Bayesian analysis
Computer Science
Health care
Image analysis
Image enhancement
Mental health
Mental health care
Occlusion
Optimization techniques
Suicides & suicide attempts
Support vector machines
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Summary:Accurately assessing suicide risk is a critical concern in mental health care. Traditional methods, which often rely on self-reporting and clinical interviews, are limited by their subjective nature and may overlook non-verbal cues. This study introduces an innovative approach to suicide risk assessment using facial image analysis. The Suicidal Visual Indicators Prediction (SVIP) Framework leverages EfficientNetb0 and ResNet architectures, enhanced through Bayesian optimization techniques, to detect nuanced facial expressions indicating mental state. The models’ interpretability is improved using GRADCAM, Occlusion Sensitivity, and LIME, which highlight significant facial regions for predictions. Using datasets DB1 and DB2, which consist of full and cropped facial images from social media profiles of individuals with known suicide outcomes, the method achieved 67.93% accuracy with EfficientNetb0 on DB1 and up to 76.6% accuracy with a Bayesian-optimized Support Vector Machine model using ResNet18 features on DB2. This approach provides a less intrusive, accessible alternative to video-based methods and demonstrates the substantial potential for early detection and intervention in mental health care.
ISSN:1573-7462
0269-2821
1573-7462
DOI:10.1007/s10462-024-10882-4