AI-Powered Brain Tumor Detection using Deep Learning and MRI Analysis

A cutting-edge web application that leverages artificial intelligence to detect brain tumors from MRI scans with 97% accuracy. Built with Flask backend and interactive web interface, this system assists medical professionals in early diagnosis and treatment planning.

• AI-Powered Detection: Deep Learning CNN model trained on thousands of MRI scans
• High Accuracy: 97% accuracy rate on validated datasets
• Instant Results: Get analysis results in less than 5 seconds
• User-Friendly Interface: Intuitive web interface for easy image upload and analysis
• Medical-Grade Analysis: Processes 8 million parameters for comprehensive evaluation
• Patient Information Tracking: Store and display patient name and analysis details
• Responsive Design: Works seamlessly on desktop, tablet, and mobile devices

• Modern Bootstrap 5 Design: Clean, professional interface
• Animated Components: Smooth transitions and floating shapes for visual appeal
• Real-time Results Display: Comprehensive result visualization with confidence metrics
• Downloadable Reports: Print-friendly result summaries for medical records
• Navigation Menu: Quick access to home, features, analysis, and resources
• Social Media Integration: Links to developer's professional profiles

• Secure Processing: Images processed securely without permanent storage
• Data Privacy: Uploaded images are temporary and cleared after analysis
• Medical Compliance: Designed with healthcare privacy in mind

• Python: 3.8 or higher
• RAM: 4GB minimum (8GB recommended)
• Disk Space: 2GB for model and dependencies
• GPU (Optional): NVIDIA GPU with CUDA support for faster processing

• Windows 10/11, macOS, or Linux
• pip (Python Package Manager)
• Virtual Environment (recommended)

# Clone the repository
git clone https://github.com/ParasPKP/brain-tumor-detection-using-ml.git

# On Windows
python -m venv venv
venv\Scripts\activate

# On macOS/Linux
python3 -m venv venv
source venv/bin/activate

pip install -r requirements.txt

python -c "import tensorflow; import flask; import cv2; print('All dependencies installed successfully!')"

The application uses the Brain Tumor Detection dataset from Kaggle.

Dataset Link: Brain Tumor Detection Dataset

1. Create Kaggle Account

   • Visit kaggle.com
   • Sign up for a free account

2. Download Dataset

   • Go to the dataset link above
   • Click "Download" button
   • Extract the downloaded ZIP file

3. Dataset Information

   • Total Images: 7,000+ MRI scans
   • Image Format: JPG, PNG
   • Image Size: Typically 224x224 pixels
   • Classes: No Tumor and Tumor
   • Split: Training/Testing ratio

# Make sure virtual environment is activated
# On Windows: venv\Scripts\activate
# On macOS/Linux: source venv/bin/activate

# Run the Flask application
python app.py

1. Open your web browser
2. Navigate to: http://localhost:5000
3. You should see the NeuroAI Detector homepage

# Run on specific port
python app.py --port 8000

# Run on network (accessible from other machines)
# Edit app.py and change: app.run(host='0.0.0.0', port=5000)

• Open http://localhost:5000 in your web browser
• You'll see the NeuroAI Detector homepage

• Click on "Analyze" in the navigation menu or "Start Analysis" button
• Scroll to the upload section

1. Enter the Patient Name (or test identifier)
2. Select an MRI scan image file:
   • Accepted formats: JPG, PNG
   • Recommended size: 224x224 pixels
   • Maximum file size: 10MB
3. Click "Analyze Image"

• The system will process the image
• Results display includes:
  • Tumor Status: "TUMOR DETECTED" or "NO TUMOR DETECTED"
  • AI Confidence: Confidence level of prediction
  • Technical Metrics: Model accuracy, parameters, processing time
  • Patient Information: Name and analysis timestamp
  • Analyzed Image: Display of processed MRI scan

• New Analysis: Perform another analysis
• Print Results: Print the results for medical records
• Medical Resources: Access additional medical information

IMPORTANT MEDICAL NOTICE:

This AI analysis tool is designed to assist medical professionals only and should NOT be used as:

• A replacement for professional medical diagnosis
• Standalone clinical decision support
• Medical treatment recommendation tool
• Primary diagnostic tool

Always consult with qualified healthcare providers for:

• Proper diagnosis and interpretation
• Treatment planning and decisions
• Medical management of brain tumors

Early detection and professional medical care are crucial for optimal patient outcomes. This tool is intended to supplement, not replace, professional medical judgment.

• Brain Tumor Diagnosis - Mayo Clinic
• Understanding Brain Tumors - Mayo Clinic
• MRI Technology for Brain Imaging
• Brain Tumor Treatment Options

• CNN in Medical Imaging
• TensorFlow Documentation
• Keras Models
• Flask Documentation

• Kaggle Brain Tumor Dataset

• GitHub Repository

Paras Parshuramkar

• 🔗 GitHub
• 💼 LinkedIn

Built with ❤️ for medical innovation and early disease detection

This project is licensed under the MIT License - see the LICENSE file for details.

Contributions are welcome! Please feel free to:

• Report bugs
• Suggest improvements
• Submit pull requests
• Fork the repository

For issues, questions, or suggestions:

1. Check the troubleshooting section above
2. Review the GitHub repository issues
3. Contact the developer through GitHub