NEUROVISION-AI: Alzheimer's Disease Detection Using Multimodal Deep Learning
Validation accuracy achieved by our hybrid CNN-RNN model
Detects Normal, Mild, Moderate, and Severe stages
Combines spatial (MRI) and temporal (cognitive) analysis
Area under the curve for robust disease detection
Early and accurate detection of Alzheimer's Disease (AD) is critical for effective intervention, a task where traditional methods often fall short. This paper presents a multimodal deep learning framework to address this diagnostic challenge.
Problem Statement
Traditional diagnostic protocols often fail to provide definitive diagnosis until the disease has advanced, limiting intervention effectiveness.
Solution
Hybrid CNN-RNN architecture combining spatial MRI features with temporal cognitive patterns for comprehensive AD staging.
| Model | Input Modality | Accuracy | F1-Score | AUC |
|---|---|---|---|---|
| CNN (Baseline) | MRI Only | 94% | 0.92 | 0.95 |
| RNN (Baseline) | Cognitive Data Only | 90% | 0.89 | 0.91 |
| Hybrid CNN-RNN (Proposed) | MRI + Cognitive | 99% | 0.98 | 0.99 |
Key Achievements
- 99% validation accuracy on four-stage classification
- 5-9% improvement over single-modality baselines
- Minimal overfitting with aligned training/validation curves
Clinical Impact
- Enables early intervention during Mild Cognitive Impairment (MCI) stage
- Reduces diagnostic delay by detecting subtle patterns
- Supports personalized treatment planning through staging
Try Our Live Implementation
Experience the power of our research through our interactive assessment tools. Test the same models described in this paper.