README.md

---
license: mit
language:
  - en
pipeline_tag: image-segmentation
tags:
  - unet
  - segmentation
  - medical-imaging
  - covid19
  - ct-scans
---

# 🩺 UNet Model for COVID-19 CT Scan Segmentation

## 📌 Model Overview
This UNet-based segmentation model is designed for **automated segmentation of COVID-19 infected lung regions** in **CT scans**. It enhances the classic **U-Net** with **attention mechanisms** to improve focus on infected regions.

- **Architecture:** UNet + Attention Gates
- **Dataset:** COVID-19 CT scans from **Coronacases.org, Radiopaedia.org, and Zenodo Repository**
- **Task:** Image Segmentation (Lung Infection)
- **Metrics:** Dice Coefficient, IoU, Hausdorff Distance, ASSD

---

## 📊 Training Details
- **Dataset Size:** 20 CT scans (512 × 512 × 301 slices)
- **Preprocessing:**
  - Normalization of pixel intensities `[0,1]`
  - HU Thresholding: `[-1000, 1500]`
  - Image resizing to `128 × 128 pixels`
  - Binarization of masks (0 = background, 1 = infected regions)
- **Augmentation:**
  - **Rotations**: ±5 degrees
  - **Elastic transformations, Gaussian blur**
  - **Brightness/contrast variations**
  - Final dataset: **2,252 CT slices**
- **Training:**
  - **Optimizer:** Adam (`learning rate = 1e-4`)
  - **Loss Function:** Weighted BCE-Dice Loss + Surface Loss
  - **Batch Size:** 16
  - **Epochs:** 25
  - **Training Platform:** NVIDIA Tesla T4 (Google Colab Pro)

---

## 🚀 Model Performance
| Metric                     | Non-Augmented Model | Augmented Model |
|----------------------------|---------------------|-----------------|
| **Dice Coefficient**       | 0.8502              | **0.8658**      |
| **IoU (Mean)**             | 0.7445              | **0.8316**      |
| **ASSD (Symmetric Distance)** | 0.3907          | **0.3888**      |
| **Hausdorff Distance**      | 8.4853              | **9.8995**      |
| **ROC AUC Score**          | 0.91                | **1.00**        |

📌 **Key Findings:**  
✔ **Augmentation improved segmentation accuracy significantly**  
✔ **Attention U-Net outperformed other segmentation models**

---

## 📥 **How to Use the Model**
### **1️⃣ Load the Model**
#### **TensorFlow/Keras**
```python
import os
from huggingface_hub import hf_hub_download
from tensorflow.keras.models import load_model
from keras.saving import register_keras_serializable
import tensorflow.keras.backend as K

# ✅ Set Keras backend (optional)
os.environ["KERAS_BACKEND"] = "jax"

# ✅ Register and define missing functions
@register_keras_serializable()
def dice_coef(y_true, y_pred, smooth=1e-6):
    y_true_f = K.flatten(y_true)
    y_pred_f = K.flatten(y_pred)
    intersection = K.sum(y_true_f * y_pred_f)
    return (2. * intersection + smooth) / (K.sum(y_true_f) + K.sum(y_pred_f) + smooth)

@register_keras_serializable()
def gl_sl(*args, **kwargs):
    pass  # Placeholder function (update if needed)

# ✅ Download the model from Hugging Face
model_path = hf_hub_download(repo_id="amal90888/unet-segmentation-model", filename="unet_model.keras")

# ✅ Load the model with registered custom objects
unet = load_model(model_path, custom_objects={"dice_coef": dice_coef, "gl_sl": gl_sl}, compile=False)

# ✅ Recompile with fresh optimizer and correct loss function
from tensorflow.keras.optimizers import Adam
unet.compile(optimizer=Adam(learning_rate=1e-4), loss="binary_crossentropy", metrics=["accuracy", dice_coef])

print("✅ Model loaded and recompiled successfully!")