Tensor Network Neurological Predictor

---
name: tensor-network-neurological-predictor
description: "Tensor Network Feature Engineering methodology for multi-class neurological disorder prediction from MRI data. Uses tensor network decompositions to extract high-dimensional features from sparse medical imaging. Activation: tensor network MRI, neurological disorder prediction, tensor feature engineering, multi-class brain disorder, MRI tensor decomposition."
---

# Tensor Network Feature Engineering for Neurological Disorder Prediction

Multi-class neurological disorder prediction using tensor network feature engineering from sparse MRI imaging data.

## Core Concept

MRI scans for neurological disorders often use sparse imaging techniques to reduce scan time. Tensor network methods can extract rich features from these sparse representations, enabling accurate multi-class disorder classification.

**Source**: arXiv:2605.17771 (May 2026) — Balakrishna et al. "Multi-Class Neurological Disorder Prediction with Tensor Network Feature Engineering". Evaluated on 55,160 images across 8 diagnostic categories using 5-fold nested stratified cross-validation. Both higher and lower PARAFAC rank configurations achieve strong performance, demonstrating robustness to tensor network expressivity.

## PARAFAC CP Decomposition Approach

The paper uses PARAFAC CP (Canonical Polyadic) tensor decomposition, which is mathematically inspired by quantum neural network architectures but implemented entirely classically:

## Architecture

### Tensor Representation

```python
import numpy as np
from tensorly.decomposition import tucker, cp
import tensorly as tl

def build_mri_tensor(mri_slices, num_slices=64):
    """Build 3D tensor from MRI slice data.
    
    Args:
        mri_slices: List of 2D MRI slices
        num_slices: Target number of slices
    
    Returns:
        tensor: 3D tensor (height, width, depth)
    """
    h, w = mri_slices[0].shape
    tensor = np.zeros((h, w, num_slices))
    for i, slice_data in enumerate(mri_slices[:num_slices]):
        tensor[:, :, i] = slice_data
    return tensor
```

### Tensor Network Decomposition

```python
def extract_tensor_features(tensor, rank=(16, 16, 8)):
    """Extract features using Tucker decomposition.
    
    Args:
        tensor: 3D MRI tensor
        rank: Target ranks for each mode
    
    Returns:
        features: Flattened feature vector from core tensor
    """
    tensor_tl = tl.tensor(tensor)
    
    # Tucker decomposition
    core, factors = tucker(tensor_tl, rank=rank)
    
    # Flatten core tensor as features
    features = tl.to_numpy(core).flatten()
    return features
```

### Multi-Class Classification Pipeline

```python
from sklearn.svm import SVC
from sklearn.model_selection import cross_val_score

def classify_disorders(features, labels, n_classes=3):
    """Classify neurological disorders using tensor features.
    
    Args:
        features: (n_samples, n_features) tensor features
        labels: Disorder class labels
        n_classes: Number of disorder classes
    
    Returns:
        accuracy: Cross-validation accuracy
        model: Trained classifier
    """
    model = SVC(kernel='rbf', decision_function_shape='ovo')
    scores = cross_val_score(model, features, labels, cv=5)
    model.fit(features, labels)
    return scores.mean(), model
```

## Workflow

1. **Data Preprocessing**:
   - Load sparse MRI data
   - Normalize intensity values
   - Handle missing slices via interpolation

2. **Tensor Construction**:
   - Stack 2D slices into 3D tensor
   - Apply spatial normalization if needed
   - Handle varying resolutions

3. **Feature Extraction**:
   - Apply Tucker/CP decomposition
   - Extract core tensor features
   - Optionally add handcrafted features

4. **Classification**:
   - Train multi-class classifier
   - Use cross-validation for evaluation
   - Handle class imbalance

5. **Interpretation**:
   - Analyze factor matrices for brain regions
   - Map important features to anatomical locations

## Parameters

- **Tucker Rank**: (16, 16, 8) for typical MRI resolution
- **Classifier**: SVM with RBF kernel or Random Forest
- **Cross-validation**: 5-fold or leave-one-out
- **Preprocessing**: Intensity normalization, skull stripping

## Advantages

- **Handles Sparsity**: Works well with reduced MRI acquisition
- **Captures 3D Structure**: Preserves spatial relationships
- **Multi-Class**: Supports multiple disorder types simultaneously
- **Interpretable**: Factor matrices reveal important brain regions

## Use Cases

- Alzheimer's disease detection
- Parkinson's disease classification
- Multiple sclerosis identification
- Brain tumor classification
- Multi-disorder differential diagnosis

## References

- Balakrishna et al. (2026). "Multi-Class Neurological Disorder Prediction with Tensor Network Feature Engineering" (arXiv:2605.17771)

## Related Skills

- quantum-ml-healthcare
- medical-ai-diagnosis
- tensor-network-medical-imaging