This project focuses on brain tumor segmentation using deep learning techniques, specifically leveraging a fine-tuned UNet architecture.

All necessary dependencies can be installed using the requirements.txt file:

pip install -r requirements.txt

The dataset used for this project can be accessed via Kaggle. It contains MRI scans with corresponding tumor segmentation masks.

A UNet model was fine-tuned on the above dataset for tumor segmentation. The trained model is available in this repository:

📍 Best Model Checkpoint

To run the GUI, follow these simple steps:

1️⃣ Clone this repository:

git clone https://github.com/your-repo/brain-tumor-segmentation.git
cd brain-tumor-segmentation

2️⃣ Install dependencies:

pip install -r requirements.txt

3️⃣ Run the GUI using Streamlit:

streamlit run main.py

Home Screen	Output Screen

For training details, refer to the Training Notebook.

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