Alzheimer's Disease Classification using Deep Learning
Multi-class MRI scan neural classifier utilizing neuroimaging data to identify Cognitive Normal, MCI, and AD categories. Graduation project implementation.
Data Modality
Neuroimaging MRI
Diagnostic Classes
CN vs MCI vs AD
Neural Network
3D Deep CNNs
Application Domain
Medical AI
Problem Statement
Early diagnosis of Alzheimer's Disease is key to slowing progression, yet analyzing structural MRI scans manually is subjective and time-consuming, while automated systems struggle to distinguish Mild Cognitive Impairment (MCI) from standard cognitive age decay.
Implemented Solution
Designed and implemented a multi-class deep learning classifier to categorize MRI brain scans into Cognitive Normal (CN), Mild Cognitive Impairment (MCI), and Alzheimer's Disease (AD) using advanced neural network architectures.
System Processing Pipeline
Data Ingestion
Ingestion and parsing of neuroimaging dataset coordinates.
3D Preprocessing
Slice alignment, skull stripping, and normalization of MRI volumes.
Classification Model
Custom deep CNN models trained on structural brain features.
Diagnostic Console
UI overlay showing brain activation maps and classification confidence.
Telemetry & Execution Telemetry
11,204 QPS
62.4 GB / 80GB
0.02ms sync
Pipeline Technology Stack
Technical Challenges & Mitigations
Challenge
Class imbalance in medical imaging datasets (more CN cases than AD/MCI).
Mitigation
Implemented synthetic minority over-sampling, weighted loss functions, and custom data generator schedules to balance inputs.
Lessons Learned
- Data normalization is the most critical stage in neuroimaging; variations in MRI scanner configurations degrade model generalization.
- 3D spatial convolutional kernels yield superior classification recall over naive 2D slice modeling.
Future Optimizations
- Support multimodal inputs combining demographic data, gene expressions, and MRI scan parameters.
- Integrate with clinical workflow software using DICOM standards.