---
license: mit
tags:
- medical
- anemia-detection
- healthcare
- sklearn
- pytorch
library_name: sklearn
pipeline_tag: image-classification
---

# Nail Anemia Detector

A machine learning model for detecting anemia from fingernail images using handcrafted color features.

## Model Description

This model analyzes fingernail images to predict anemia risk. It extracts medically-relevant color features (pallor indices, redness measures, hemoglobin proxies) and uses an MLP classifier for prediction.

**Key Features:**
- 100% recall mode available (catches all anemic patients)
- Lightweight and fast inference
- Suitable for screening applications

## Performance

| Metric | Value |
|--------|-------|
| AUC | 0.75 |
| Accuracy | 73% |
| Recall (Sensitivity) | 90% |
| Specificity | 46% |
| F1 Score | 0.81 |

### High Sensitivity Mode
For 100% recall (no missed anemic patients):
- Set threshold to 0.255
- Specificity: 17.1%
- Use for screening followed by confirmatory blood test

## Model Files

| File | Description |
|------|-------------|
| `mlp_model.joblib` | Trained sklearn MLP classifier |
| `feature_scaler.joblib` | StandardScaler for feature normalization |
| `best_model.pt` | PyTorch model (alternative) |
| `model_metadata.json` | Model configuration and metrics |
| `inference.py` | Inference script |

## Usage

### Installation

```bash
pip install joblib numpy pandas pillow scikit-learn scipy
```

### Quick Inference

```python
import joblib
import numpy as np
from PIL import Image
from scipy.ndimage import uniform_filter

# Load model
model = joblib.load('mlp_model.joblib')
scaler = joblib.load('feature_scaler.joblib')

# Threshold: 0.10 for balanced, 0.255 for 100% recall
threshold = 0.10

def extract_features(img):
    img = img.convert('RGB').resize((224, 224))
    arr = np.array(img).astype(float)
    r, g, b = arr[:,:,0], arr[:,:,1], arr[:,:,2]
    brightness = 0.299*r + 0.587*g + 0.114*b
    
    features = []
    
    # Brightness features
    features.extend([brightness.mean(), brightness.std()])
    features.extend([np.percentile(brightness, p) for p in [10, 25, 50, 75, 90]])
    
    # Redness features
    redness = r / (r + g + b + 1e-10)
    features.extend([redness.mean(), redness.std()])
    
    # Pallor features
    white_ratio = (brightness > 180).sum() / brightness.size
    pink_ratio = ((r > 150) & (g < 150) & (b < 150)).sum() / brightness.size
    features.extend([white_ratio, pink_ratio])
    
    # Channel statistics
    for ch in [r, g, b]:
        features.extend([ch.mean(), ch.std()])
    
    # Color ratios
    features.extend([
        (r.mean() + 1) / (g.mean() + 1),
        (r.mean() + 1) / (b.mean() + 1),
        (r.mean() - b.mean()) / 255,
    ])
    
    # Hemoglobin proxy
    hb = r / (g + b + 1)
    features.extend([hb.mean(), hb.std()])
    
    # Spatial features
    h, w = arr.shape[:2]
    top = brightness[:h//3, :].mean()
    bottom = brightness[2*h//3:, :].mean()
    features.append(top - bottom)
    
    center = brightness[h//4:3*h//4, w//4:3*w//4].mean()
    features.append(center - brightness.mean())
    
    # Gradient features
    gx = np.abs(np.diff(brightness, axis=1, prepend=brightness[:, :1]))
    gy = np.abs(np.diff(brightness, axis=0, prepend=brightness[:1, :]))
    gradient = np.sqrt(gx**2 + gy**2)
    features.extend([gradient.mean(), gradient.std()])
    
    # Local variance
    local_mean = uniform_filter(brightness, size=7)
    local_var = uniform_filter((brightness - local_mean)**2, size=7)
    local_var = np.maximum(local_var, 0)
    features.extend([np.sqrt(local_var).mean(), np.sqrt(local_var).std()])
    
    return np.array(features, dtype=np.float32)

# Predict
img = Image.open('nail_image.jpg')
features = extract_features(img)
features_scaled = scaler.transform(features.reshape(1, -1))
probability = model.predict_proba(features_scaled)[0, 1]

prediction = "anemia" if probability >= threshold else "healthy"
print(f"Prediction: {prediction}")
print(f"Probability: {probability:.4f}")
```

## Training Details

### Dataset

This model was trained on the **Detection of Anemia using Colour of the Fingernails Image Datasets from Ghana**:

> Asare, Justice Williams; APPIAHENE, PETER; DONKOH, EMMANUEL (2022), "Detection of Anemia using Colour of the Fingernails Image Datasets from Ghana", Mendeley Data, V1, doi: 10.17632/2xx4j3kjg2.1

Dataset available at: https://data.mendeley.com/datasets/2xx4j3kjg2/1

### Training Data
- 3,406 images from 443 patients
- Patient-level splits to prevent data leakage
- Features: 27 handcrafted color features

### Feature Engineering
Features extracted based on medical knowledge:
- **Pallor indicators**: Brightness metrics, white/pink ratios
- **Redness measures**: R channel statistics, redness index
- **Color ratios**: R/G, R/B, hemoglobin proxies
- **Saturation**: Color intensity measures
- **Spatial patterns**: Gradients, center vs edge differences

### Model Architecture
- MLP with hidden layers: [128, 64]
- Optimizer: Adam
- Loss: Cross-entropy with class weights

## Limitations

1. **Not a diagnostic tool**: Use only for screening, confirm with blood tests
2. **Limited dataset**: ~550 patients
3. **Specificity trade-off**: High sensitivity comes with more false positives
4. **Single imaging site**: Only fingernail images tested

## Intended Use

This model is designed as a **pre-screening tool** for anemia in:
- Primary care settings
- Resource-limited areas
- Telemedicine applications

**Workflow:**
```
Image Analysis (this model) → If positive → Blood test confirmation
```

## Ethical Considerations

- Results should be confirmed by healthcare professionals
- Not intended to replace standard diagnostic procedures
- Consider population demographics when interpreting results

## Citation

If you use this model, please cite both the model and the dataset:

**Model:**
```bibtex
@model{nail_anemia_detector,
  title={Nail Anemia Detector},
  author={JetX-GT},
  year={2026},
  publisher={Hugging Face},
  url={https://huggingface.co/JetX-GT/nail-anemia-detector}
}
```

**Dataset:**
```bibtex
@dataset{asare2022anemia,
  title={Detection of Anemia using Colour of the Fingernails Image Datasets from Ghana},
  author={Asare, Justice Williams and Appiahene, Peter and Donkoh, Emmanuel},
  year={2022},
  publisher={Mendeley Data},
  version={V1},
  doi={10.17632/2xx4j3kjg2.1}
}
```

## License

MIT License