Deep Hybrid Models for Early Diagnosis of Mental Health Conditions
DOI:
https://doi.org/10.15662/IJEETR.2026.0802424Keywords:
Mental Health Detection, Multimodal Learning, Deep Learning, Transformer Models, LSTM, CNN, Explainable AI, Early DiagnosisAbstract
Mental health disorders are increasingly prevalent across populations, creating a strong need for diagnostic systems that are both accurate and capable of identifying early-stage symptoms before they escalate. Conventional assessment methods such as clinical interviews, self-report questionnaires, and behavioral observations often face limitations including subjectivity, inconsistency, and delayed detection of emotional changes. These challenges highlight the necessity for more advanced, objective, and timely diagnostic approaches.
The proposed Deep Hybrid Multimodal Framework presents an intelligent and unified solution by integrating multiple data modalities, including text, speech, and facial expressions. It leverages transformer-based natural language processing models to analyze linguistic patterns, LSTM networks to capture temporal dynamics in speech, and CNN or Vision Transformer models to interpret facial behavior. This multimodal integration enables the system to detect subtle emotional indicators associated with conditions such as depression, anxiety, and stress.
A key feature of the framework is its hybrid attention-fusion mechanism, which combines information from different modalities to generate context-aware insights. This approach enhances predictive performance by capturing interdependencies between text, audio, and visual signals. Additionally, the incorporation of Explainable AI techniques, such as attention heatmaps and token-level interpretability, ensures transparency in decision-making, making the system more reliable and suitable for clinical applications.
The system is designed with modular pipelines that process text, audio, and video inputs efficiently and can be deployed through scalable microservices. This architecture makes it well-suited for real-time applications, particularly in telehealth environments where continuous monitoring is essential. Overall, the framework provides a robust pathway for early detection, proactive intervention, and improved mental health outcomes on a global scale
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