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---
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!")
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