Enhancing Multimodal Fusion Techniques for Depression Detection

Dual Objectives

Primary Research Goal

Develop an advanced multimodal deep learning framework for accurate depression detection utilizing CNN architectures and sophisticated fusion techniques across multiple data modalities.

Clinical Application

Build a comprehensive counselor channeling web platform integrated with intelligent chatbot-based depression screening to bridge the gap between detection and treatment.

Research Team

Team Members

Supervisors

Table of Contents

  1. Executive Summary
  2. Literature Review
  3. Research Methodology
  4. System Architecture
  5. Implementation Details
  6. Experimental Results
  7. Clinical Impact
  8. Future Work
  9. Publications
  10. Links

Executive Summary

Problem Statement

Current depression detection systems suffer from limited accuracy, poor generalization across diverse populations, and insufficient integration of multimodal data sources. Existing approaches fail to meet clinical requirements for reliable mental health assessment.

Innovation Approach

This research introduces a novel hybrid multimodal fusion framework that intelligently combines textual, audio, visual, and clinical data streams through advanced deep learning architectures. Our approach addresses key limitations through:

Expected Impact

Literature Review

Current State of Research

Multimodal Detection Approaches

Modality-Specific Techniques

Modality Current Methods Limitations
Text LSTM, BERT, GPT-based models Limited emotional context understanding
Audio CNN, LSTM, Wav2Vec approaches Poor generalization across demographics
Video CNN, ResNet-50 architectures Insufficient temporal feature capture
Clinical MIMIC-III dataset utilization Bias handling and data scarcity issues

Identified Research Gaps

Research Methodology

Comprehensive Multimodal Framework

Our methodology employs a sophisticated deep learning architecture combining CNNs with advanced target-level fusion techniques:

Data Acquisition and Preprocessing

Primary Datasets:

Data Modalities:

Feature Extraction Pipeline

Modality Extraction Method Key Features
Text LIWC + HuggingFace Transformers Sentiment analysis, linguistic patterns, semantic embeddings
Audio EDIAOZ Framework Pitch variation, MFCC coefficients, jitter, shimmer, prosodic features
Video OpenFace Toolkit Facial Action Units (FAUs), eye gaze patterns, head pose dynamics
Clinical BioBERT Embeddings Medical terminology understanding, clinical note analysis

Advanced Fusion Strategies

Multi-Level Fusion Approach:

Model Architecture Design

Core Components:

System Architecture

Early Fusion Architecture

Early Fusion Model Architecture

Design Philosophy: The Early Fusion model creates unified multimodal representations by combining features from all input modalities at the initial processing stage. This architecture enables the model to learn complex inter-modal relationships from the ground up, potentially capturing subtle correlations between different data streams that might be missed in later fusion approaches.

Key Advantages:

Late Fusion Architecture

Late Fusion Model Architecture

Design Philosophy: The Late Fusion model processes each modality through specialized, independent neural networks before combining their outputs at the decision level. This approach allows for modality-specific optimization and specialized feature extraction, with each pathway contributing its expertise to the final diagnostic decision.

Key Advantages:

Hybrid Fusion Framework

Advanced Integration Strategy: Our novel hybrid approach combines the strengths of both early and late fusion through an adaptive weighting mechanism that learns optimal combination strategies based on input characteristics and modality reliability.

Implementation Details

Technology Stack

Core ML Framework

Web Application Stack

Development Workflow

# Environment Setup
conda create -n lumithrive python=3.10
conda activate lumithrive
pip install -r requirements.txt

# Model Training Pipeline
python train_multimodal_model.py --config configs/hybrid_fusion.yaml

# Web Application Deployment
docker-compose up --build

Model Training Pipeline

Training Configuration:

Data Augmentation Strategies:

Experimental Results

Performance Metrics

Note: Results will be updated following completion of experimental phase

Evaluation Framework:

Expected Performance Targets:

Ablation Studies

Planned Experiments:

  1. Modality Importance Analysis: Individual and combined modality performance
  2. Fusion Strategy Comparison: Early vs. Late vs. Hybrid fusion effectiveness
  3. Architecture Sensitivity: Impact of network depth and attention mechanisms
  4. Dataset Generalization: Cross-dataset validation and transfer learning

Clinical Impact

Healthcare Integration

Clinical Workflow Enhancement:

Accessibility Improvements

Democratizing Mental Health Care:

Ethical Considerations

Responsible AI Development:

Future Work

Research Extensions

Technical Innovations

Advanced Modeling Approaches:

Publications

Publications will be added upon completion of experimental phases and peer review process