NeuroTumorNet

A deep learning model for brain tumor classification using MRI images.

Description

🧠 Classify brain tumors with NeuroTumorNet! 🩻 Powered by a CNN built with TensorFlow 🤖, this tool analyzes MRI scans to detect Glioma, Meningioma, Pituitary, or No Tumor. 🚀 Upload an image via the Streamlit UI 🌐 and get instant predictions with confidence scores! ✨ Download the model or explore the live demo and datasets below. 🖥️

Overview

NeuroTumorNet is a CNN-based tool that classifies brain MRI images into four categories:

• Glioma tumor
• Meningioma tumor
• No tumor
• Pituitary tumor

The model uses a convolutional neural network architecture built with TensorFlow and Keras to provide accurate tumor classification.

🔍 Features

• Automatic detection and classification of brain tumors
• Support for multiple tumor types (glioma, meningioma, pituitary)
• User-friendly web interface for image upload and analysis
• High accuracy brain tumor classification using convolutional neural networks

📋 Table of Contents

• Installation
• Usage
• Model
• Dataset
• License
• Support
• Contributors

🔧 Installation

Prerequisites

• Python 3.7+
• pip (Python package installer)

Steps

1. Clone the repository:

git clone https://github.com/haybnzz/NeuroTumorNet/
cd NeuroTumorNet

2. Install required dependencies:

pip install -r requirements.txt

3. Download the pre-trained model:
   • Option 1: Download directly from Hugging Face:

     wget "https://huggingface.co/haydenbanz/NeuroTumorNet/resolve/main/brain_tumor_model.h5?download=true" -O brain_tumor_model.h5

   • Option 2: Use the provided script to download and prepare the model:

     python data_to_model.py

• Option 3: Download directly from Kaggle:

Dataset (Optional)

  Donload from above Badge section 
  ```
If you want to train the model yourself or test it with the original dataset, you can download the brain tumor MRI dataset from the provided data link in the repository.

## Usage

### Running the Web Application

1. After installation, start the web application:
```bash
python app.py

2. Open your browser and navigate to:

http://localhost:5000

3. Upload an MRI image through the web interface to get the tumor classification result.

NeuroTumorNet/
├── app.py               # Web application for tumor classification
├── data_to_model.py     # Script to download and prepare the model
├── requirements.txt     # Dependencies list
├── brain_tumor_model.h5 # Pre-trained model file
├
└── README.md            # This file

🧠 Model

NeuroTumorNet uses a deep convolutional neural network architecture designed specifically for medical image classification. The model architecture consists of:

• Multiple convolutional layers with ReLU activation
• Max pooling layers for feature extraction
• Dropout layers to prevent overfitting
• Dense layers for classification

The pre-trained model achieves high accuracy in classifying the four categories of brain MRI images.

📊 Dataset

The model was trained on a dataset containing brain MRI images categorized into four classes:

• Glioma tumor
• Meningioma tumor
• Pituitary tumor
• No tumor (normal brain MRI)

To download the dataset for training or testing purposes, visit one of these sources:

• Kaggle Brain Tumor Dataset
• Figshare Brain Tumor Dataset

After downloading, place the dataset in a folder named dataset with the following structure:

dataset/
├── Training/
│   ├── glioma_tumor/
│   ├── meningioma_tumor/
│   ├── no_tumor/
│   └── pituitary_tumor/
└── Testing/
    ├── glioma_tumor/
    ├── meningioma_tumor/
    ├── no_tumor/
    └── pituitary_tumor/

Image Display

Here are the images from the repository:

1. 
2. 
3. 

📜 License

This project is licensed under the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) License. See the LICENSE file for more details.

Unauthorized use is strictly prohibited.

📧 Contact: singularat@protn.me

☕ Support

Donate via Monero: 45PU6txuLxtFFcVP95qT2xXdg7eZzPsqFfbtZp5HTjLbPquDAugBKNSh1bJ76qmAWNGMBCKk4R1UCYqXxYwYfP2wTggZNhq

👥 Contributors and Developers

📝 Citation

If you use NeuroTumorNet in your research, please cite:

@software{NeuroTumorNet2025,
  author = {Haybnzz and Glitchesminds},
  title = {NeuroTumorNet: Deep Learning for Brain Tumor Classification},
  url = {https://github.com/haybnzz/NeuroTumorNet},
  year = {2025},
}

@misc {hay.bnz_2025,
	author       = { {Hay.Bnz} },
	title        = { NeuroTumorNet (Revision 7f9585f) },
	year         = 2025,
	url          = { https://huggingface.co/haydenbanz/NeuroTumorNet },
	doi          = { 10.57967/hf/4899 },
	publisher    = { Hugging Face }
}

Acknowledgments

• Thanks to all contributors to the brain tumor MRI datasets used in training this model
• Built with TensorFlow and Keras