brain-tumor-segmentation

Model Card for Brain Tumor MRI Segmentation Model

A deep-learning model for brain tumor segmentation in MRI scans using a U-Net-based convolutional neural network. The model outputs a binary mask identifying tumor regions and is intended for research and educational use only.

Model Details

Model Description

This model performs pixel-wise segmentation of brain tumors from MRI slices.
It was developed as part of an academic computer vision project focusing on medical image segmentation.
The architecture is a U-Net implemented in TensorFlow/Keras, trained on the Brain Tumor MRI dataset by Jun Cheng (2017), available on Figshare (DOI: 10.6084/m9.figshare.1512427.v8). The dataset contains MRI scans labeled for tumor segmentation and is commonly used in medical image analysis research.

• Developed by: Yara Elshehawi
• Model type: U-Net convolutional neural network for image segmentation
• License: Apache-2.0

Model Sources

• Repository (training code)
• Hugging Face Model
• Demo

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Uses

Direct Use

The model can be used to:

• Segment tumor regions in MRI slices
• Support research in medical image processing
• Serve as a reference implementation for U-Net segmentation tasks
• Be integrated into pipelines requiring binary mask prediction

Downstream Use (Optional)

• Fine-tuning on other medical segmentation datasets
• Adaptation to multi-class tumor segmentation
• Integration into educational tools or visualization dashboards

Out-of-Scope Use

The model must not be used for:

• Real clinical diagnosis or medical decision-making
• Automated medical treatment recommendations
• High-stakes deployment on real patient data without proper validation and certification

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Bias, Risks, and Limitations

• The model is trained on a limited dataset, which may not generalize to all MRI scanners, protocols, or patient populations.
• Segmentation quality may vary depending on image contrast, noise, or MRI orientation.
• Not suitable for real-world clinical environments without extensive validation.

Recommendations

Users should:

• Avoid using the model on out-of-domain MRI scans
• Conduct their own evaluation before using it in any research pipeline
• Be aware that predictions may include false positives/negatives

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How to Get Started with the Model

You can use this model in two ways:

1. Load it directly from Hugging Face

from tensorflow.keras.models import load_model
from keras import backend as K
import tensorflow as tf
import numpy as np
from tensorflow.keras.preprocessing import image


# Dice Coefficient (required for custom_objects)

def dice_coef(y_true, y_pred):
    y_true = K.cast(y_true, 'float32')
    y_pred = K.cast(y_pred, 'float32')
    y_true_f = K.flatten(y_true)
    y_pred_f = K.flatten(y_pred)
    intersect = K.sum(y_true_f * y_pred_f)
    return (2. * intersect + K.epsilon()) / (
        K.sum(y_true_f) + K.sum(y_pred_f) + K.epsilon()
    )

# Load the model from Hugging Face
model = load_model(
    "yaraa11/brain-tumor-segmentation",
    custom_objects={"dice_coef": dice_coef},
    compile=False
)

# (Optional) Recompile
model.compile(
    optimizer="adam",
    loss="binary_crossentropy",
    metrics=[
        "accuracy",
        tf.keras.metrics.Precision(),
        tf.keras.metrics.Recall(),
        dice_coef
    ]
)

# Load and preprocess input image
img = image.load_img(
    "path_to_image.png",
    target_size=(224, 224),
    color_mode="grayscale"
)
img = image.img_to_array(img) / 255.0
img = np.expand_dims(img, axis=0)

# Predict mask
prediction = model.predict(img)

# Convert to binary mask
binary_mask = (prediction > 0.5).astype(np.float32)

1. Or load it locally if you downloaded the .keras file

from tensorflow.keras.models import load_model
import numpy as np
from tensorflow.keras.preprocessing import image

model = load_model(
    "./model.keras",
    custom_objects={"dice_coef": dice_coef},
    compile=False
)


# Load and preprocess input image
img = image.load_img(
    "path_to_image.png",
    target_size=(224, 224),
    color_mode="grayscale"
)
img = image.img_to_array(img) / 255.0
img = np.expand_dims(img, axis=0)

# Predict mask
prediction = model.predict(img)

# Convert to binary mask
binary_mask = (prediction > 0.5).astype(np.float32)

Training Details

Training Data

The model was trained on a publicly available brain MRI dataset. All images were preprocessed to ensure consistency across the dataset, including:

• Resizing to 256 × 256
• Normalizing intensities to [0, 1]
• Converting images to single-channel (grayscale) format
• Splitting the dataset into training, validation, and testing subsets

Training Procedure

Preprocessing

Before training, each MRI slice was:

• Normalized to [0, 1]
• Resized to 256 × 256
• Converted to grayscale (single-channel) format

Training Hyperparameters

• Loss function: Binary Cross-Entropy
• Optimizer: Adam
• Learning rate: 0.001
• Batch size: 16
• Epochs: 40

The model was trained end-to-end using TensorFlow/Keras.

Speeds, Sizes, Times

• Model size: 6 MB
• Training time: Approximately 1–3 hours on a single Colab GPU (e.g., T4)
• Number of parameters: 487,009 (~1.86 MB)

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Evaluation

Testing Data, Factors & Metrics

Testing Data

Evaluation was conducted on a held-out portion of the MRI dataset that was not used during training.
All preprocessing steps were identical to those used during training.

Factors

Model performance may vary depending on:

• Tumor size and location
• MRI contrast variations
• Image noise, artifacts, or motion
• Differences in scanning equipment or acquisition protocols

Metrics

The model was evaluated using standard medical image segmentation metrics:

• Accuracy
• Precision
• Recall
• Intersection over Union (IoU)
• Dice Coefficient (primary metric for tumor segmentation)

Results

• Dice: 0.7019
• IoU: 0.4996
• Precision: 0.7860
• Recall: 0.7714

Summary

The model performs strongly on the provided dataset and reliably segments tumor regions.
However, performance may degrade on MRI scans from different sources or with significantly different characteristics.

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Model Examination

The U-Net architecture uses skip connections that allow fine-grained spatial information to be preserved, improving boundary definition and tumor localization.
Performance is influenced heavily by input image quality and consistent preprocessing.

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Environmental Impact

• Hardware Type: Single NVIDIA T4 GPU (Google Colab)
• Training Duration: Approximately 8 hours
• Cloud Provider: Google Colab
• Estimated Carbon Emissions: <0.2 kg CO₂ (low-impact training)

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Technical Specifications

Model Architecture and Objective

• Architecture: U-Net encoder–decoder CNN
• Objective: Binary semantic segmentation (tumor vs. non-tumor)
• Activation: Sigmoid output for pixel-wise classification
• Framework: TensorFlow/Keras

Compute Infrastructure

Hardware

• Google Colab GPU (T4 / RTX class)

Software

• Python 3.10
• TensorFlow / Keras
• NumPy, OpenCV, SciPy

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Citation ()

@article{Cheng2017,
author = "Jun Cheng",
title = "{brain tumor dataset}",
year = "2017",
month = "4",
url = "https://figshare.com/articles/dataset/brain_tumor_dataset/1512427",
doi = "10.6084/m9.figshare.1512427.v8"
}

Glossary

Dice Coefficient:
A segmentation metric measuring overlap between predicted and ground-truth masks. Ranges from 0 (no overlap) to 1 (perfect overlap). Commonly used in medical imaging.

IoU (Intersection over Union):
A metric evaluating how well predicted regions match the true regions by dividing the intersection area by the union area.

Segmentation Mask:
A binary image where each pixel is labeled as either tumor (1) or background (0).

U-Net:
A convolutional neural network architecture with an encoder–decoder structure and skip connections, widely used for biomedical image segmentation.

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More Information

For additional details, sample predictions, or troubleshooting, please refer to the repository’s README or open a discussion on the Hugging Face model page.

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Model Card Authors

Created by Yara Elshehawi, as part of a medical image segmentation project focused on brain tumor detection using MRI data.

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Contact Information

If you have any questions, encounter issues, or are interested in collaboration, feel free to reach out through any of the following channels:

• Hugging Face Repository Discussion Board: Visit the "Community" section of the model page to start a discussion or post an issue.
• Portfolio Website: You can also contact me directly through my portfolio website for additional inquiries.

Looking forward to hearing from you!