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Identifying mental health status using deep neural network trained by visual metrics

Medicine and Health

Identifying mental health status using deep neural network trained by visual metrics

S. B. Shafiei, Z. Lone, et al.

This innovative study, conducted by Somayeh B. Shafiei, Zaeem Lone, Ahmed S. Elsayed, Ahmed A. Hussein, and Khurshid A. Guru, presents an objective method for mental health evaluation through a CNN-LSTM model utilizing visual metrics time-series data. With impressive classification accuracy rates, this research opens the door for at-home mental health monitoring applications.

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Playback language: English
Abstract
This study proposes an objective method for mental health evaluation using a CNN-LSTM model trained on visual metrics time-series data. Data were collected from 16 cancer patients and 9 controls using TobiiPro eyeglasses while viewing artwork. Mental health metrics (hope, anxiety, well-being) were assessed using questionnaires. The model achieved high classification accuracy (93.81%, 94.76%, and 95.00% for hope, anxiety, and well-being, respectively) and could be integrated into home-based mental health monitoring applications.
Publisher
Translational Psychiatry
Published On
Authors
Somayeh B. Shafiei, Zaeem Lone, Ahmed S. Elsayed, Ahmed A. Hussein, Khurshid A. Guru
Tags
mental health
CNN-LSTM model
visual metrics
time-series data
classification accuracy
home-based monitoring
artwork

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