https://github.com/repo-bilalnaeem/brain-segmentation

This project focuses on the segmentation of brain tumors using the Brain Tumor Segmentation (BRATs) dataset. The primary goal was to develop a deep learning model capable of accurately identifying and segmenting tumor regions in MRI scans.
https://github.com/repo-bilalnaeem/brain-segmentation

brainsegmentation brats-challenge brats-dataset image-classification image-processing image-segmentation mri-images tumor tumor-detection tumor-segmentation unet-image-segmentation

Last synced: about 1 month ago
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This project focuses on the segmentation of brain tumors using the Brain Tumor Segmentation (BRATs) dataset. The primary goal was to develop a deep learning model capable of accurately identifying and segmenting tumor regions in MRI scans.

• Host: GitHub
• URL: https://github.com/repo-bilalnaeem/brain-segmentation
• Owner: repo-bilalnaeem
• Created: 2024-04-20T15:48:15.000Z (10 months ago)
• Default Branch: main
• Last Pushed: 2024-05-19T11:49:02.000Z (9 months ago)
• Last Synced: 2024-12-12T16:40:28.752Z (2 months ago)
• Topics: brainsegmentation, brats-challenge, brats-dataset, image-classification, image-processing, image-segmentation, mri-images, tumor, tumor-detection, tumor-segmentation, unet-image-segmentation
• Language: Jupyter Notebook
• Homepage:
• Size: 129 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# Brain Tumor Segmentation using BRATs Dataset

## Introduction

This project focuses on the segmentation of brain tumors using the Brain Tumor Segmentation (BRATs) dataset. The primary goal was to develop a deep learning model capable of accurately identifying and segmenting tumor regions in MRI scans.

## Project Highlights

- **Advanced Data Preprocessing**: Cleaned and preprocessed MRI scans to ensure high-quality input data.

- **Innovative Model Development**: Created and fine-tuned state-of-the-art deep learning models for tumor segmentation.

- **Performance Optimization**: Enhanced model accuracy and reduced computation time through various optimization techniques.

- **Comprehensive Analysis**: Conducted thorough validation to assess model performance.

## Methodology

### Data Preprocessing

- Loaded and normalized the MRI scans.

- Augmented the dataset to improve model robustness.

- Split the data into training, validation, and test sets.

#### Sample Preprocessed Images

Here are some examples of preprocessed MRI scans:

![Preprocessed Image 1](Pre-processed-output-1.png)

![Preprocessed Image 2](Pre-processed-output-2.png)

### Model Development

- Used a U-Net architecture for segmentation.

- Implemented techniques such as data augmentation and dropout to prevent overfitting.

- Trained the model using cross-entropy loss and the Adam optimizer.

### Performance Metrics

- **Accuracy**: Achieved an accuracy of 95%.

- **Dice Similarity Coefficient (DSC)**: Attained a DSC of 92%, surpassing the baseline by 10%.

- **Inference Time Reduction**: Reduced inference time by 30%.

## Results

### Training and Validation Loss

The graph below shows the training and validation loss over epochs:

![Training and Validation Loss](Training-Validation-Loss.png)

### Training and Validation Accuracy

The graph below shows the training and validation accuracy over epochs:

![Training and Validation Accuracy](Tainining-Validation-Accuracy.png)

### Sample Predictions

Here are some sample outputs from the segmentation model:

![Prediction Image 1](Predicted-Outputs.png)

## Usage

### Prerequisites

- Python 3.7+

- Jupyter Notebook

- Required libraries: `numpy`, `pandas`, `tensorflow`, `keras`, `sklearn`, `matplotlib`

### Installation

Clone the repository:

```bash

git https://github.com/billu2002/Brain-Segmentation.git