Early-Onset Alzheimer’s Detection Using Multi-Modal Data Fusion of Retinal Imaging, Speech Patterns, and Genomic Data

This repository hosts the official research and implementation of a cutting-edge Multi-Modal Data Fusion Framework engineered for the ultra-early detection of Early-Onset Alzheimer's Disease. By cross-analyzing and fusing three non-invasive, distinct biological markers—Retinal Images (Computer Vision), Speech Patterns (Audio/NLP), and Genomic Data (Bioinformatics)—this framework is designed to identify subtle neurodegenerative patterns up to 5 years before conventional cognitive symptoms manifest.

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📌 Research Vision & Core Concept

Single-modality diagnostic tools (like relying only on MRI scans) often catch Alzheimer's when significant, irreversible brain damage has already occurred. This project introduces a holistic, multi-perspective approach:

• Retinal Imaging: Tracking microvascular changes and nerve fiber layer thinning in the eye (acting as a direct window to the brain).
• Speech Patterns: Evaluating acoustic degradation, pauses, and linguistic choices using speech-to-text and acoustic feature extractors.
• Genomic Data: Analyzing Single Nucleotide Polymorphisms (SNPs) and high-risk genetic variations (such as the APOE ε4 allele).
• Cross-Modal Data Fusion: Merging these heterogeneous feature spaces using advanced Cross-Attention mechanisms or Early/Late Fusion layers.

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🛠️ Key Features & Methodology

1. Multi-Modal Feature Extractors: Custom neural pipelines featuring CNNs/Vision Transformers (for Retinal Images), Wav2Vec/BERT (for Speech), and Feed-Forward Embedding nets (for Genomics).
2. Advanced Fusion Network: Implements a Transformer-based cross-attention mechanism that learns correlation weights across different data modalities.
3. Imbalanced Class Optimization: Custom loss functions (Focal Loss/Contrastive Loss) optimized for early-stage diagnostic data scarcity.
4. Gold Standard Benchmarking: Ready-made integration pipelines for world-renowned medical repositories including the ADNI (Alzheimer's Disease Neuroimaging Initiative) and UK Biobank.

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📂 Repository Structure

├── src/
│   ├── data_processors/    # Separate data loaders for Images, Audio, and Genomic text
│   ├── encoders/           # Specific feature extractors (Vision, NLP, Bio)
│   ├── fusion_layers/      # Cross-attention and multi-modal merging pipelines
│   └── evaluation/         # ROC-AUC curves, Confusion Matrices, and F1 scoring
├── data/                   # Preprocessing configurations for ADNI data splits
├── configs/                # Hyperparameters for training epochs and fusion types
├── notebooks/              # Exploratory data fusion and feature visualization
├── Literature_Review/      # Team research matrices and BibTeX references
└── README.md