logo
ResearchBunny Logo
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.

00:00
00:00
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

Related Publications

Explore these studies to deepen your understanding of the subject.

Brain-optimized deep neural network models of human visual areas learn non-hierarchical representations
Cognitive Science

Brain-optimized deep neural network models of human visual areas learn non-hierarchical representations

G. St-yves, E. J. Allen, et al.

Unraveling the Neural Network: Identifying Temporal Labeling of Visual Events through EEG-Based Functional Connectivity Analysis of Brain Regions
Biology

Unraveling the Neural Network: Identifying Temporal Labeling of Visual Events through EEG-Based Functional Connectivity Analysis of Brain Regions

S. Khoonbani and H. Ramezanian

Preferences for and intention to use an app for premenstrual mental health symptoms using the Health Behaviour Model (HBM)
Medicine and Health

Preferences for and intention to use an app for premenstrual mental health symptoms using the Health Behaviour Model (HBM)

E. L. Funnell, N. A. Martin-key, et al.

Temporal dynamics in mental health symptoms and loneliness during the COVID-19 pandemic in a longitudinal probability sample: a network analysis
Psychology

Temporal dynamics in mental health symptoms and loneliness during the COVID-19 pandemic in a longitudinal probability sample: a network analysis

M. Odenthal, P. Schlechter, et al.

Listen, Learn & Level Up
Over 10,000 hours of research content in 25+ fields, available in 12+ languages.
No more digging through PDFs, just hit play and absorb the world's latest research in your language, on your time.
listen to research audio papers with researchbunny