September 5, 2025Open Access

Emotion-Aware ResNet50V2: Enhancing Mental Health Detection through Facial Expression Analysis

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Authors

PLPuspen Lahiri RDR. DeyJadavpur University TJTithi Jana

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Implication

This research demonstrates an emotion-aware classifier's ability to improve accuracy in mental health detection, suggesting advancements in diagnosis and treatment planning.

Key Points

The model achieves 90.03% accuracy in detecting facial emotions, enhancing mental health diagnosis.
Using the FER2013 dataset, the emotion-aware classifier correlates detected feelings with mental health conditions effectively.
The approach shows robustness against noisy data, outperforming state-of-the-art techniques in emotional recognition.
This research underscores the significant role of deep learning in personalized mental health treatment strategies.

Cite This Study

Lahiri et al. (2025) studied this question.

synapsesocial.com/papers/68c23966b210217d6477c3d7 https://doi.org/10.21203/rs.3.rs-7236314/v1

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Implication

This research demonstrates an emotion-aware classifier's ability to improve accuracy in mental health detection, suggesting advancements in diagnosis and treatment planning.

Emotion-Aware ResNet50V2: Enhancing Mental Health Detection through Facial Expression Analysis

Discussion

Member takes

Implication

Key Points

Cite This Study

Emotion-Aware ResNet50V2: Enhancing Mental Health Detection through Facial Expression Analysis

Discussion

Member takes

Implication

Key Points

Cite This Study

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