NeuroScan

This Streamlit web application classifies brain tumor types based on MRI images. It uses a pre-trained Convolutional Neural Network (CNN) model to predict the presence and type of brain tumor, including Glioma, Meningioma, Pituitary tumors, or No tumor.

Website Link : Click here

🛠 Features

• User Input: Upload an MRI image of the brain (grayscale).
• Model Prediction: Classifies the brain tumor into one of the four classes:
  • Glioma (Class 0)
  • Meningioma (Class 1)
  • No Tumor (Class 2)
  • Pituitary Tumor (Class 3)
• Prediction Probability: Displays the probability of each classification.
• Image Preprocessing: Automatically converts color images to grayscale and resizes them to match the model’s input dimensions.

🚀 Getting Started

Prerequisites

To run this project locally, you'll need:

• Python 3.7+
• pip (Python package manager)
• Streamlit for the web interface
• TensorFlow for model prediction
• Pillow for image handling

Installation

1. Create a virtual environment:

   python -m venv env
   ./env/Scripts/activate

2. Install the required Python packages:

   pip install -r requirements.txt

Running the Application

To run the Streamlit app, use the following command:

streamlit run main.py

🔧 How It Works

1. Image Upload: The user uploads an MRI image of the brain.
2. Preprocessing: The app converts the uploaded image to grayscale (if necessary) and resizes it to the required dimensions (300x300).
3. Model Prediction: The app uses a pre-trained CNN model to classify the tumor type (Glioma, Meningioma, No Tumor, or Pituitary).
4. Results Display: The app shows the predicted tumor class and the probability of the classification.

📊 Model Used

The app uses a Convolutional Neural Network (CNN) to classify brain tumors into one of four categories:

• Glioma (Class 0)
• Meningioma (Class 1)
• No Tumor (Class 2)
• Pituitary Tumor (Class 3)

The CNN model consists of several layers:

• Convolutional Layers: Extract features from the input images.
• Max Pooling Layers: Reduce the spatial dimensions.
• Fully Connected Layers: Learn high-level representations of the features.
• Softmax Output Layer: Produces probabilities for each of the four classes.

The model is trained using a dataset of MRI images of brains with various tumor types and is designed to predict the tumor type based on new inputs.

📂 Directory Structure

Brain-Tumor-Classification-App/
├── .streamlit/
├── brain-tumor-dataset/
├── .gitattributes
├── .gitignore
├── brain-tumor-classification.ipynb
├── brain-tumor-model.h5
├── train.py
├── main.py
├── requirements.txt
└── README.md

🔧 Dependencies

All required dependencies are listed in the requirements.txt file. You can install them using:

pip install -r requirements.txt

Key dependencies:

streamlit numpy tensorflow matplotlib

📸 Screenshots

App Interface:

Prediction Results:

🧑‍💻 How to Train the Model (Optional)

• Run the train.py script:

python train.py

This will retrain the model and save the updated brain-tumor-model.h5 file.

🔄 Example

Input:

• MRI Image of a brain scan (upload your own image).

Output:

Prediction: Meningioma
Prediction Probability: 99.59%

💬 Feedback & Contributions

Feel free to open an issue or pull request if you encounter any problems or have suggestions for improvements.

🎉 Acknowledgments

• Streamlit: A great framework for creating web applications.
• Keras/TensorFlow: Used for training and building the neural network model.

👨‍💻 Created By

Vivek Sharma.

Thank you for checking out the project!