Property	Details
🏗️ Architecture	U-Net
🎯 Task	Semantic Segmentation
🖼️ Input	Lung CT scan slices (grayscale)
🎭 Output	Binary mask — infected vs. healthy tissue
📐 Input size	256 × 256 px
⚙️ Framework	PyTorch
📜 License	MIT

🧠 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.

📈 Performance

Metric	Score
Dice Coefficient	> 0.85
Task	Binary segmentation (infected / healthy)

🚀 How to Use

python

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}%")

🗂️ 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

⚠️ 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