Integrating Multimodal Data with CNN and LSTM Models: A Paradigm Shift in Mental Health Diagnostics

Abstract

It is not surprising that mental health disorders are not simple and may need multiple forms of data to diagnose and treat the patient. In this paper, a promising concept of quantifying mental health disorder symptoms through the fusion of multiple modalities using CNNs and LSTMs is described. CNNs are particularly effective in recognizing spatial characteristics that can be extracted from picture data, be it a MRI image of the brain, a facial expression or a person’s gesture, for instance, while LSTMs are quite successful in recognizing temporal patterns within time series data, such as in speech and signal processing, or in behavioural and physiological data over time. The proposed method is more comprehensive and dynamic since the two classes of deep learning models are integrated to estimate the severity of the mental health problem. As a research question, we propose the concept of how fMRI images, audios, and heart rate variability can be used together to improve the process of recognizing people’s mental states. It is proposed that the signs related to spatial features of data and temporal groups be identified through CNNs for spatial feature extraction and LSTMs for temporal sequence analysis, the system can provide more accurate predictions and for different mental health states, including depression, anxiety, and PTSD. This research seeks to show that the application of deep learning could improve the diagnosis of mental health disorders, and tailor treatment to patient preferences. The findings of the following study demonstrate enhanced accurate diagnoses, which indicate a bright future for AI-based treatment of mental disorders