Semantic-Segmentation-COVID-19

🫁 U-Net — COVID-19 Lung CT Semantic Segmentation

U-Net based semantic segmentation of COVID-19 infected regions in lung CT scans.

⚠️ For research and educational purposes only — not a medical device.

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📌 Model Summary

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🧠 What This Model Does

This U-Net model segments COVID-19 infected regions in lung CT scan slices, producing a binary mask that highlights infected tissue in red overlay.

It was developed as part of a deep learning research project exploring medical image analysis — combining classical encoder-decoder architecture with modern training techniques for robust segmentation performance.

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📈 Performance

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🚀 How to Use

import torch
from torchvision import transforms
from PIL import Image
import numpy as np

# Load model
model = torch.jit.load("unet_model.pt", map_location="cpu")
model.eval()

# Preprocessing
transform = transforms.Compose([
    transforms.Resize((256, 256)),
    transforms.Grayscale(),
    transforms.ToTensor(),
    transforms.Normalize([0.5], [0.5]),
])

# Inference
image = Image.open("ct_scan_slice.png")
tensor = transform(image).unsqueeze(0)

with torch.no_grad():
    output = model(tensor)
    mask = torch.sigmoid(output).squeeze().numpy()

# Binary mask
binary_mask = (mask > 0.5).astype(np.uint8) * 255

# Percentage of infected area
infected_pct = binary_mask.mean() / 255 * 100
print(f"Infected area: {infected_pct:.1f}%")

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🗂️ Training Data

• Dataset: COVID-19 CT scan dataset (public research datasets)
• Input format: 256×256 greyscale CT slices
• Labels: Binary masks — infected lung regions vs. healthy tissue

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⚠️ Limitations & Disclaimer

• Not a medical device — do not use for clinical diagnosis or treatment decisions
• Validated on research datasets only — not on clinical production data
• Performance may vary across different CT scanner manufacturers and imaging protocols
• Always consult qualified medical professionals for clinical interpretation

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🔗 Related Resources

• 🤗 Live Demo Space
• 💻 GitHub Repository

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👤 Author

Martin Badrous — Computer Vision & Deep Learning Engineer