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amal90888/
unet-segmentation-model

Name: unet-segmentation-model
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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

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!")