Brain Tumor Detection Using Machine Learning Source Code ( Final Year)

Admin Features

• Dataset and Preprocessing:
• The project uses a brain tumor segmentation dataset available publicly on the internet. The dataset includes 3064 MRI images and their corresponding 3064 segmentation masks.
• A shell script (download_data.sh) is provided to download the dataset automatically.
• A preprocessing script (mat_to_numpy.py) is included to convert the downloaded data into a usable format for the model. This script converts the .mat files into numpy arrays, which are easier to manipulate in the Python environment.
• Model Architecture:
• The model is based on the U-Net architecture, which is specifically designed for biomedical image segmentation.
• U-Net consists of a contracting path to capture context and a symmetric expanding path for precise localization, making it ideal for segmentation tasks.
• The model uses "Conv2D" layers for the contraction and "Conv2DTranspose" layers for upsampling (expansion).
• Loss Function and Metrics:
• The project utilizes a combination of multiple loss functions, including binary cross-entropy and Dice loss, each given equal weightage. This combination is effective for pixel-level classification tasks.
• The Intersection over Union (IoU) metric is used to evaluate the model's performance. IoU measures the overlap between the predicted segmentation and the ground truth.
• Training and Optimization:
• The model is trained using the Adam optimizer, which is a variant of the gradient descent optimization technique, known for its efficiency and adaptability.
• The learning rate decays from "1e-3 to 1e-4" over the course of 40-60 epochs to ensure better convergence.
• A horizontal flip image augmentation technique is applied to increase the diversity of training samples and improve the model’s generalization.
• Model Performance and Evaluation:
• The U-Net model is evaluated using stratified train-test splits based on the type of tumor (meningioma, glioma, pituitary tumor).
• Detailed segmentation results for the three types of tumors are demonstrated in the provided examples.

User Features

• Dataset and Preprocessing:
• The project uses a brain tumor segmentation dataset available publicly on the internet. The dataset includes 3064 MRI images and their corresponding 3064 segmentation masks.
• A shell script (download_data.sh) is provided to download the dataset automatically.
• A preprocessing script (mat_to_numpy.py) is included to convert the downloaded data into a usable format for the model. This script converts the .mat files into numpy arrays, which are easier to manipulate in the Python environment.
• Model Architecture:
• The model is based on the U-Net architecture, which is specifically designed for biomedical image segmentation.
• U-Net consists of a contracting path to capture context and a symmetric expanding path for precise localization, making it ideal for segmentation tasks.
• The model uses "Conv2D" layers for the contraction and "Conv2DTranspose" layers for upsampling (expansion).
• Loss Function and Metrics:
• The project utilizes a combination of multiple loss functions, including binary cross-entropy and Dice loss, each given equal weightage. This combination is effective for pixel-level classification tasks.
• The Intersection over Union (IoU) metric is used to evaluate the model's performance. IoU measures the overlap between the predicted segmentation and the ground truth.
• Training and Optimization:
• The model is trained using the Adam optimizer, which is a variant of the gradient descent optimization technique, known for its efficiency and adaptability.
• The learning rate decays from "1e-3 to 1e-4" over the course of 40-60 epochs to ensure better convergence.
• A horizontal flip image augmentation technique is applied to increase the diversity of training samples and improve the model’s generalization.
• Model Performance and Evaluation:
• The U-Net model is evaluated using stratified train-test splits based on the type of tumor (meningioma, glioma, pituitary tumor).
• Detailed segmentation results for the three types of tumors are demonstrated in the provided examples.

Other Features

• Dataset and Preprocessing:
• The project uses a brain tumor segmentation dataset available publicly on the internet. The dataset includes 3064 MRI images and their corresponding 3064 segmentation masks.
• A shell script (download_data.sh) is provided to download the dataset automatically.
• A preprocessing script (mat_to_numpy.py) is included to convert the downloaded data into a usable format for the model. This script converts the .mat files into numpy arrays, which are easier to manipulate in the Python environment.
• Model Architecture:
• The model is based on the U-Net architecture, which is specifically designed for biomedical image segmentation.
• U-Net consists of a contracting path to capture context and a symmetric expanding path for precise localization, making it ideal for segmentation tasks.
• The model uses "Conv2D" layers for the contraction and "Conv2DTranspose" layers for upsampling (expansion).
• Loss Function and Metrics:
• The project utilizes a combination of multiple loss functions, including binary cross-entropy and Dice loss, each given equal weightage. This combination is effective for pixel-level classification tasks.
• The Intersection over Union (IoU) metric is used to evaluate the model's performance. IoU measures the overlap between the predicted segmentation and the ground truth.
• Training and Optimization:
• The model is trained using the Adam optimizer, which is a variant of the gradient descent optimization technique, known for its efficiency and adaptability.
• The learning rate decays from "1e-3 to 1e-4" over the course of 40-60 epochs to ensure better convergence.
• A horizontal flip image augmentation technique is applied to increase the diversity of training samples and improve the model’s generalization.
• Model Performance and Evaluation:
• The U-Net model is evaluated using stratified train-test splits based on the type of tumor (meningioma, glioma, pituitary tumor).
• Detailed segmentation results for the three types of tumors are demonstrated in the provided examples.

How to run Brain Tumor Detection Using Machine Learning

• Step 3: Install Python

• Ensure you have Python 3.x installed on your machine. If not, you can download and install Python from here.
• After installation, verify it by typing the following command:
• python --version
• It should show the Python version.

• Step 4: Set Up a Virtual Environment (Optional but Recommended)

• Creating a virtual environment helps isolate the dependencies for the project.
• Navigate to the project directory:
• cd brain-tumor-segmentation-unet
• Create a virtual environment:
• python -m venv env
• Activate the virtual environment:
• .\env\Scripts\activate

• Step 5: Install the Required Dependencies

• pip install -r requirements.txt

• Step 6: Download the Dataset

• The project uses an MRI brain tumor segmentation dataset. There's a script that will download the dataset for you.
• Run the "download_data.sh" script to download the dataset:
• bash tumor-segmentation-unet/download_data.sh
• This will download the dataset, which contains 3064 MRI images and 3064 masks.

• Step 7: Preprocess the Data

• After downloading the dataset, convert the data into a usable format by running the "mat_to_numpy.py" script. This will convert the dataset into a NumPy array format for easier processing.
• Run the following command:
• python mat_to_numpy.py brain_tumor_dataset/

• Step 8: Run the Jupyter Notebook

• The project code is in a Jupyter Notebook (brain_tumor_segmentation.ipynb). You will need Jupyter installed to run it. Since you already installed the dependencies, Jupyter Notebook should be available.
• Run the following command to start the Jupyter Notebook:
• jupyter notebook
• This will open a Jupyter Notebook interface in your browser. Navigate to the "brain_tumor_segmentation.ipynb" file and open it.
• Now you can run each cell step by step.

• Step 9: Training the Model

• Once the notebook is open, you can run all the cells in sequence. This will:
• Load the data
• Preprocess the data
• Build the U-Net model
• Train the model on the brain tumor segmentation dataset

• Step 10: Viewing the Results

• Once the model is trained, you will see the segmentation results on MRI images of the brain. The U-Net will highlight tumor regions in the MRI scans.

Credentials

Not Required

License

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