https://github.com/hiranmayikolambe/brain-tumor-detection-using-multiresolution-analysis
This project uses Gabor filters and 3D U-Net to detect and segment brain tumors from MRI scans using the BraTS 2020 dataset
https://github.com/hiranmayikolambe/brain-tumor-detection-using-multiresolution-analysis
3d-unet brain-tumor-detection brats2020 deep-learning gabor-filters medical-imaging mri multiresolution python segmentation semantic-segmentation
Last synced: 2 months ago
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This project uses Gabor filters and 3D U-Net to detect and segment brain tumors from MRI scans using the BraTS 2020 dataset
- Host: GitHub
- URL: https://github.com/hiranmayikolambe/brain-tumor-detection-using-multiresolution-analysis
- Owner: hiranmayikolambe
- Created: 2025-01-04T16:21:55.000Z (5 months ago)
- Default Branch: main
- Last Pushed: 2025-01-04T16:35:44.000Z (5 months ago)
- Last Synced: 2025-01-26T04:14:02.043Z (4 months ago)
- Topics: 3d-unet, brain-tumor-detection, brats2020, deep-learning, gabor-filters, medical-imaging, mri, multiresolution, python, segmentation, semantic-segmentation
- Language: Python
- Homepage:
- Size: 1.38 MB
- Stars: 0
- Watchers: 2
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# MRI Brain Tumor Detection Project
## Overview
This project implements a brain tumor detection system using **Gabor filters** and a **3D U-Net architecture**. The model is trained on the **BraTS 2020 dataset**, which contains multimodal MRI images (FLAIR, T1, T1CE, T2) and corresponding segmentation masks. The goal is to accurately segment brain tumors from MRI scans.
## Table of Contents
1. [Requirements](#requirements)
2. [Dataset](#dataset)
3. [Installation](#installation)
4. [Usage](#usage)
- [Gabor Filter Visualization](#gabor-filter-visualization)
- [Model Training](#model-training)
- [Model Evaluation](#model-evaluation)
5. [License](#license)
## Requirements
To run this project, you will need the following Python packages:
- `numpy`
- `opencv-python`
- `matplotlib`
- `nibabel`
- `tensorflow`
- `keras`
- `sklearn`
- `segmentation-models-3D`
You can install the required packages using pip:
```bash
pip install numpy opencv-python matplotlib nibabel tensorflow keras scikit-learn segmentation-models-3D
```
## Dataset
This project uses the **BraTS 2020 dataset**, which can be downloaded from the BraTS Challenge website. The dataset contains the following modalities:
- FLAIR
- T1
- T1CE
- T2
- Segmentation masks
Make sure to place the dataset in the appropriate directory as specified in the code.
## Installation
1. Clone this repository or download the notebook file.
2. Ensure that the BraTS dataset is downloaded and placed in the specified directory.
3. Open the notebook in **Google Colab** or **Jupyter Notebook**.
## Usage
### Load and Preprocess Data
The code loads the MRI images and corresponding masks, normalizes the pixel values, and prepares the data for training.
### Gabor Filter Application
The code applies **Gabor filters** to the MRI images to enhance feature extraction.
### Model Definition
A **3D U-Net** model is defined for semantic segmentation of brain tumors.
### Training
The model is trained using the training dataset, and the training history is recorded.
### Evaluation
The model's performance is evaluated using metrics such as **Intersection over Union (IoU)** and accuracy.
## Gabor Filter Visualization
The project includes a section that visualizes Gabor filters with varying parameters (theta and gamma). This helps in understanding how different filter configurations affect the feature extraction process.
Example code to visualize Gabor filters:
```python
# Example of creating Gabor filters
ksize = 50
sigma = 3
thetas = [0, np.pi/4, np.pi/2, 3*np.pi/4]
gammas = [1, 0.5, 0.01]
# Code to visualize Gabor filters
```
## Model Training
The model is trained using the following parameters:
- **Batch size**: 2
- **Learning rate**: 0.0001
- **Number of epochs**: 100
The training process includes data augmentation and uses a custom loss function that combines **Dice loss** and **focal loss** to handle class imbalance.
Example code for model training:
```python
# Example of model training
history = model.fit(train_img_datagen,
steps_per_epoch=steps_per_epoch,
epochs=100,
validation_data=val_img_datagen,
validation_steps=val_steps_per_epoch)
```
## Model Evaluation
After training, the model's performance is evaluated on a validation dataset. The code calculates the **Mean IoU** and visualizes the predictions against the ground truth masks.
Example code for model evaluation:
```python
# Example of model evaluation
IOU_keras = MeanIoU(num_classes=n_classes)
IOU_keras.update_state(test_pred_batch_argmax, test_mask_batch_argmax)
print("Mean IoU =", IOU_keras.result().numpy())
```
## License
This project is licensed under the **MIT License**. See the LICENSE file for more details.