Brain Tumor Segmentation

This project focuses on detecting and segmenting brain tumors from MRI images using deep learning.

Project Description

Brain tumor segmentation is an important task in medical image analysis.
The goal of this project is to automatically identify tumor regions in brain MRI scans and separate them from normal brain tissue.

The project uses a neural network-based approach to process MRI images and generate segmentation masks.
These masks can help visualize the location and shape of tumors more clearly.

Features

• Brain tumor segmentation from MRI images.
• Deep learning-based model for medical image analysis.
• Output segmentation masks for tumor regions.
• Useful for research and educational purposes.

Technologies Used

• Python
• Deep Learning
• TensorFlow / PyTorch
• OpenCV
• NumPy
• Matplotlib

Dataset

The model is trained and tested on brain MRI datasets.
You can replace the dataset path with your own MRI data if needed.

How It Works

1. Load brain MRI images.
2. Preprocess the images.
3. Train the segmentation model.
4. Predict tumor regions on test images.
5. Generate segmentation masks and results.

Results

The model can highlight possible tumor areas in MRI scans and help in visual inspection of the segmented regions.

Future Improvements

• Improve segmentation accuracy.
• Add better visualization tools.
• Support more MRI modalities.
• Deploy as a web application for easier use.

Conclusion

This project provides a basic framework for brain tumor segmentation using deep learning.
It can be extended for further research and clinical image analysis.