---
license: apache-2.0
tags:
- image-classification
- medical
- malaria
- africa
- tanzania
- dinov2
- blood-smear
- imbalanced-classification
datasets:
- harvard-dataverse-malaria-tanzania
metrics:
- balanced_accuracy
- mcc
- recall
- auc
base_model: facebook/dinov2-base
---

# Malaria Detector — DINOv2 fine-tuned on Tanzania Blood Smears 🦟

Fine-tuned [facebook/dinov2-base](https://huggingface.co/facebook/dinov2-base) for binary classification of blood smear images: **malaria infected** vs **healthy**.

Trained on **real patient data** from 5 Tanzanian health centres, with **rigorous handling of class imbalance**.

## 📊 Performance (test set)

| Metric | Value | Note |
|--------|-------|------|
| Accuracy | 99.44% | Can be misleading with imbalance |
| **Balanced Accuracy** | **99.25%** | ⭐ Imbalance-robust |
| **MCC (Matthews)** | **0.988** | ⭐ Best single metric |
| F1 Macro | 99.40% | Equitable across classes |
| Recall (Sensitivity) | 100.00% | ⭐ Critical clinical metric |
| Specificity | 98.51% | |
| AUC-ROC | 100.00% | |
| AUC-PR | 100.00% | Imbalance-robust AUC |

## ⚖️ Class imbalance handling

Dataset has **1.64x imbalance** (Paludisme: 62% vs Sain: 38%).

**Techniques applied** :
1. ✅ Class weights in loss function
2. ✅ Stratified split on subtype (not just binary label)
3. ✅ Aggressive data augmentation
4. ✅ MCC-based model selection (instead of accuracy)
5. ✅ Multiple imbalance-robust metrics reported

## 🚀 Quick Start

```python
from transformers import AutoImageProcessor, AutoModelForImageClassification
from PIL import Image
import torch

processor = AutoImageProcessor.from_pretrained('Sadou/malaria-detector-dinov2-tanzania')
model = AutoModelForImageClassification.from_pretrained('Sadou/malaria-detector-dinov2-tanzania')

img = Image.open('blood_smear.jpg').convert('RGB')
inputs = processor(img, return_tensors='pt')

with torch.no_grad():
    outputs = model(**inputs)

probs = torch.softmax(outputs.logits, dim=-1)[0]
pred = torch.argmax(probs).item()

labels = ['Healthy', 'Malaria']
print(f'{labels[pred]} (confidence: {probs[pred]:.1%})')
```

## 📂 Dataset

**Tanzania Malaria Blood Smear Dataset**
- Source: Harvard Dataverse
- DOI: [10.7910/DVN/O2WVWA](https://doi.org/10.7910/DVN/O2WVWA)
- Images: 3,544 real blood smears
- Source: 5 health centres in Tanga region, Tanzania
- Microscope: 4K SONY IMX334 sensor (40X-2500X)
- Staining: Giemsa reagent
- Reference: Lufyagila et al. 2024, *Data in Brief*

### Class distribution
| Class | Count |
|-------|-------|
| Thick Infected | 1,139 |
| Thick Uninfected | 1,071 |
| Thin Infected | 1,064 |
| Thin Uninfected | 270 |

## ⚠️ Disclaimer

**This is a research tool, NOT a certified medical device.** 
Any real diagnosis must be confirmed by a qualified microscopist or physician.
Clinical validation is required before any real-world deployment.

## 📚 Citation

```bibtex
@misc{malaria-dinov2-2026,
  author = {Sadou Barry},
  title = {Malaria Detector — DINOv2 fine-tuned on Tanzania Blood Smears with Class Imbalance Handling},
  year = {2026},
  publisher = {HuggingFace},
  url = {https://huggingface.co/Sadou/malaria-detector-dinov2-tanzania}
}
```