Overview
Deep learning model analyzing brain MRI scans to detect early signs of Alzheimer's disease with 93% accuracy, enabling earlier intervention and treatment planning.
The Problem
Early diagnosis of Alzheimer's is crucial for effective management, but subtle structural changes in early stages are difficult to detect visually. Traditional diagnosis relies on cognitive tests which may miss early-stage cases.
The Solution
Developed a 3D CNN model that analyzes structural MRI scans to identify subtle brain atrophy patterns characteristic of early Alzheimer's. The model focuses on hippocampal volume reduction and cortical thinning patterns.
Project Gallery
Results & Impact
93%
Classification Accuracy
On ADNI dataset
91%
Sensitivity
Early-stage detection
94%
Specificity
Healthy vs diseased
Key Impact
- Potential tool for early screening support
- Reduces diagnostic time from weeks to minutes
- Helps prioritize patients for detailed cognitive assessment
Technologies Used
PythonTensorFlow3D CNNMedical ImagingNibabelScikit-learn