---
title: RBC Morphology Classifier
emoji: 🔬
colorFrom: red
colorTo: pink
sdk: gradio
sdk_version: 3.50.2
app_file: app.py
pinned: false
license: apache-2.0
---

# 🔬 RBC Morphology Classifier

AI-powered Red Blood Cell analysis for anemia detection using deep learning.

## 🎯 Overview

This Hugging Face Space hosts a trained **EfficientNet-B0** model that classifies red blood cell (RBC) microscopy images as **healthy** or **anemic**. The model was trained on the AneRBC-II dataset containing 12,000 labeled RBC images.

## 📊 Model Performance

- **Architecture:** EfficientNet-B0 (transfer learning)
- **Training Dataset:** AneRBC-II (12,000 images)
  - 6,000 healthy RBC images
  - 6,000 anemic RBC images
- **Validation Accuracy:** 95.83%
- **Test Accuracy:** ~95%
- **Model Size:** ~16 MB
- **Input Size:** 224×224 RGB images
- **Classes:** 2 (healthy, anemic)

## 🚀 Usage

### Web Interface

1. Upload an RBC microscopy image (JPG, PNG, or BMP)
2. Click "Classify RBC"
3. View results with confidence scores and probabilities

### API Access

**Python (Gradio Client):**

```python
from gradio_client import Client

client = Client("YOUR_USERNAME/rbc-morphology-classifier")
result = client.predict("path/to/rbc_image.jpg", api_name="/predict")
print(result)
```

**Python (requests):**

```python
import requests
import base64

# Read and encode image
with open("rbc_image.jpg", "rb") as f:
    image_b64 = base64.b64encode(f.read()).decode()

# Call API
response = requests.post(
    "https://YOUR_USERNAME-rbc-morphology-classifier.hf.space/api/predict",
    json={"data": [image_b64]}
)

print(response.json())
```

**cURL:**

```bash
curl -X POST \
  https://YOUR_USERNAME-rbc-morphology-classifier.hf.space/api/predict \
  -H "Content-Type: application/json" \
  -d '{"data": ["base64_encoded_image"]}'
```

## 📈 Example Output

```json
{
  "prediction": "anemic",
  "confidence": 0.9421,
  "probabilities": {
    "healthy": 0.0579,
    "anemic": 0.9421
  },
  "model": "EfficientNet-B0",
  "dataset": "AneRBC-II (12,000 images)",
  "validation_accuracy": "95.83%"
}
```

## 🔬 Training Details

### Dataset: AneRBC-II

The model was trained on the **AneRBC-II** dataset, a benchmark dataset for computer-aided anemia diagnosis:

- **Source:** Mendeley Data
- **Total Images:** 12,000 (306×320 pixels each)
- **Classes:** Healthy (6,000) and Anemic (6,000)
- **Includes:** Original images, segmentation masks, CBC reports, morphology reports
- **License:** CC BY 4.0

### Training Configuration

- **Framework:** PyTorch 2.1.0
- **Model:** EfficientNet-B0 (pretrained on ImageNet)
- **Optimizer:** AdamW (lr=1e-4, weight_decay=1e-4)
- **Scheduler:** CosineAnnealingLR (T_max=20)
- **Loss:** CrossEntropyLoss
- **Batch Size:** 32
- **Epochs:** 20
- **Data Split:** 70% train / 15% val / 15% test
- **Augmentation:** Random flips, rotation (20°), color jitter

### Data Augmentation

Training images were augmented with:
- Random horizontal and vertical flips
- Random rotation (±20°)
- Color jitter (brightness, contrast)
- Resize to 224×224
- Normalization (ImageNet statistics)

## 📚 Citation

### Dataset Citation

If you use this model or the AneRBC dataset in your research, please cite:

```bibtex
@article{shahzad2024anerbc,
  title={AneRBC dataset: a benchmark dataset for computer-aided anemia diagnosis using RBC images},
  author={Shahzad, Muhammad and Shirazi, Syed Hamad and Yaqoob, Muhammad and Khan, Zakir and Rasheed, Assad and Sheikh, Israr Ahmed and Hayat, Asad and Zhou, Huiyu},
  journal={Database},
  volume={2024},
  pages={baae120},
  year={2024},
  publisher={Oxford University Press},
  doi={10.1093/database/baae120},
  url={https://doi.org/10.1093/database/baae120}
}
```

**Dataset Link:** https://data.mendeley.com/datasets/hms3sjzt7f/1

### Model Citation

```bibtex
@software{rbc_morphology_classifier,
  title={RBC Morphology Classifier: EfficientNet-B0 for Anemia Detection},
  author={Your Name},
  year={2024},
  url={https://huggingface.co/spaces/YOUR_USERNAME/rbc-morphology-classifier}
}
```

## ⚠️ Disclaimer

**This model is intended for research and educational purposes only.**

- **NOT FOR CLINICAL USE:** This tool should not be used as the sole basis for medical diagnosis or treatment decisions.
- **Consult Professionals:** Always seek the advice of qualified healthcare professionals for medical concerns.
- **Accuracy Limitations:** While the model achieves ~95% accuracy on test data, real-world performance may vary.
- **Bias:** Model performance depends on image quality, staining techniques, and population characteristics.

## 🔗 Related Links

- **AneRBC Dataset (Mendeley):** https://data.mendeley.com/datasets/hms3sjzt7f/1
- **Publication (Database Journal):** https://doi.org/10.1093/database/baae120
- **EfficientNet Paper:** https://arxiv.org/abs/1905.11946
- **Timm Library:** https://github.com/huggingface/pytorch-image-models

## 📄 License

- **Model:** Apache 2.0
- **Dataset (AneRBC):** CC BY 4.0
- **Code:** Apache 2.0

## 🤝 Acknowledgments

- **Dataset Authors:** Muhammad Shahzad, Syed Hamad Shirazi, Muhammad Yaqoob, et al.
- **AneRBC Team:** Shaukat Khanum Memorial Cancer Hospital and Research Centre
- **Framework:** PyTorch, Timm, Gradio, Hugging Face Spaces

## 📧 Contact

For questions, issues, or collaborations, please open an issue in the discussion tab or contact the Space owner.

---

**Built with ❤️ for medical AI research**