---
license: apache-2.0
language:
- en
metrics:
- accuracy
- recall
- precision
- IoU
- dice coefficient
pipeline_tag: image-segmentation
tags:
- brain-tumor
- tensorflow
- image-segmentation
library_name: keras
---
# 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)](https://github.com/yaraeslamm/Medical_Segmentation)
- [Hugging Face Model]( https://huggingface.co/yaraa11/brain-tumor-segmentation)
- [Demo](https://huggingface.co/spaces/yaraa11/brain-tumor-segmentation-app)

---

## 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

---

## 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  

---

## How to Get Started with the Model
You can use this model in two ways:
1. Load it directly from Hugging Face

```python
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)

```
2. Or load it locally if you downloaded the .keras file

```python
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)

---

## 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.

---

## 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.

---

## 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)

---

## 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  

---

## Citation ()

```bibtex
@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.

---

## 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.

---

## Model Card Authors

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

---

## 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](https://yaraeslamm.github.io) for additional inquiries.

Looking forward to hearing from you!