🧠 Brain Tumor Classification Using Deep Learning

Brain Tumor Classifier is a deep learning-powered Flask application that detects and classifies brain tumors from MRI images. The project integrates convolutional neural networks (CNNs) with a clean web interface to assist in medical image diagnostics.

📌 Key Features

• 🧠 Classifies MRI images into tumor types using CNNs
• 🔁 Uses Transfer Learning (e.g., VGG19) for improved performance
• 📊 Shows prediction results in real-time
• 🧪 Trained with real MRI datasets
• 🌐 Flask-powered web interface for easy usage
• 📁 Saves flagged/predicted images for analysis

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📹 Video Demo

Brain.Tumor.Classification.using.Deep.Learning.Demo.mp4

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📂 Project Structure

File/Folder	Description
📁 Brain-Tumor-Classification-Using-Deep-Learning/	Root project directory
├── Advance DL Project Brain Tumor Image Classification.ipynb	Jupyter notebook for model training and evaluation
├── archive.zip	MRI dataset archive
├── README.md	Project documentation
├── LICENSE	MIT License
├── .gitignore	Files/folders ignored by Git
📁 Brain Tumor Classification using DL/	Flask app directory
├── app.py	Flask server script with upload & prediction routes
├── flagged/image/	Stores uploaded and predicted images
📁 static/css/	Static frontend CSS files (Bootstrap grid)
├── bootstrap-grid.css	Grid system for layout
└── bootstrap-grid.css.map	Source map for the grid CSS

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🧱 Core Technologies

Tool / Library	Description
Python	Core programming language used across the project
Jupyter Notebook	Interactive environment for training and evaluation
TensorFlow	Deep learning framework used for model building
Keras	High-level neural networks API for TensorFlow
OpenCV	Image processing library for handling MRI scans
Scikit-Learn	Machine learning utilities for metrics and preprocessing
Pandas	Data manipulation and analysis
NumPy	Numerical computing and array manipulation
Matplotlib	Visualization library used for plotting graphs
Flask	Lightweight web framework for serving the model
Bootstrap	CSS framework used for basic UI layout and styling

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🧠 Model Workflow

1. Preprocess and augment MRI images (resize, normalize)
2. Use CNN with transfer learning (e.g., VGG19)
3. Train on labeled tumor categories
4. Evaluate using accuracy, confusion matrix
5. Serve model via Flask for real-time predictions

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🧪 Sample Results

• Validation Accuracy: ~95%
• Tumor Types Detected:
  • Glioma
  • Meningioma
  • Pituitary Tumor
  • No Tumor

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📸 Web App Interface

• Upload a brain MRI image (JPG, PNG)
• Get tumor prediction in seconds
• Download and flag the image for reference

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🗂 Dataset

The dataset (archive.zip) includes MRI images for 4 tumor types. It is used for both training and testing. Make sure to extract and organize as needed.

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📘 Sample Usage

Start the Flask server

Open the browser and navigate to http://localhost:5000

Upload an MRI scan

View prediction and save the result

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📄 License

MIT License – you are free to use, modify, and share with proper attribution.

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🙋 Author

Your: Sahil Mishra

GitHub: sahilmishra108

Email: sahilvatsa959@gmail.com