🏆 BITCAMP 2025 FIRST PLACE WINNER 🏆

NeuroScan.AI is an advanced brain tumor classification system that combines state-of-the-art deep learning with personalized medical recommendations. The system analyzes MRI scans, provides detailed classifications, and generates comprehensive medical reports with treatment recommendations.

• 🥇 First Place Winner at Bitcamp 2025
  • Recognized for innovative application of AI in healthcare
  • Praised for combining deep learning with Google's Gemini AI for medical recommendations
  • Highlighted for professional medical report generation and user-friendly interface

🌐 Live Demo: https://neuroscan-ai.onrender.com/

• 🔍 Advanced MRI Analysis: Utilizes deep learning (MobileNetV2) to analyze brain MRI scans
• 📊 Multi-Class Classification: Identifies different types of brain tumors with confidence scores
• 👤 Patient Information Integration: Collects and incorporates detailed patient data for personalized analysis
• 🤖 AI-Powered Recommendations: Generates personalized treatment recommendations using Google's Gemini AI
• 📄 Professional PDF Reports: Creates comprehensive medical reports with:
  • Patient information
  • MRI scan analysis
  • Classification results with confidence scores
  • Personalized treatment recommendations
  • Space for doctor's approval and notes
• ✅ Medical Validation: Includes a section for doctor's review and approval

• TensorFlow 2.15.0 (Deep Learning Framework)
• Streamlit 1.32.0 (Web Application Framework)
• OpenCV 4.9.0.80 (Image Processing)
• Pillow 10.2.0 (Image Handling)
• NumPy 1.24.3 (Numerical Operations)

NeuroScan.AI/
├── app/                    # Streamlit application directory
│   ├── app.py             # Main Streamlit application
│   └── model_utils.py     # Model loading and preprocessing utilities
├── requirements.txt        # Project dependencies
└── brain-tumor-detection-70-accuracy.py  # Training script

1. Clone the repository:

git clone https://github.com/yourusername/NeuroScan.AI.git
cd NeuroScan.AI

2. Create and activate a virtual environment:

python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

3. Install dependencies:

pip install -r requirements.txt

1. Launch the Application:

cd app
streamlit run app.py

2. Upload MRI Scan:

   • Click the "Upload MRI Scan" button
   • Select a clear, high-quality MRI image file
   • The system will automatically process and analyze the image

3. Enter Patient Information:

   • Fill in the patient details form, including:
     • Personal Information (name, age, gender)
     • Medical History
     • Current Symptoms
     • Previous Treatments
     • Family History
     • Lifestyle Factors

4. Review Analysis:

   • View the tumor classification results
   • Check confidence scores and probability distribution
   • Review AI-generated treatment recommendations

5. Generate PDF Report:

   • Click "Generate Report" to create a comprehensive medical report
   • The PDF report includes:
     • Patient information
     • MRI scan analysis
     • Classification results
     • Personalized treatment recommendations
     • Medical disclaimer
     • Doctor's approval section

6. Medical Validation:

   • Take the generated report to your healthcare provider
   • The report includes a dedicated section for doctor's review and approval
   • Your doctor can:
     • Review the AI analysis
     • Add additional notes
     • Sign and approve the recommendations
     • Add their medical license number for validation

• 🏥 Medical Disclaimer: This tool is designed to assist medical professionals, not replace them. All recommendations should be reviewed and approved by a qualified healthcare provider.
• 📋 Data Privacy: Patient information is processed locally and not stored on any external servers.
• 🔒 Security: The system uses secure, encrypted connections for all API communications.

The system uses a fine-tuned MobileNetV2 architecture trained on a comprehensive dataset of brain MRI scans. The model achieves high accuracy in classifying different types of brain tumors while maintaining efficient processing times.

This project builds upon and acknowledges the following sources:

1. Brain Tumor Classification Dataset by Sartaj Bhuvaji

   • Source: Kaggle
   • URL: Brain Tumor Classification Dataset

2. Model Development Reference

   • Author: Amirhosein Mousavian
   • Source: Kaggle
   • Accuracy: 70%
   • URL: Brain Tumor Detection

Contributions are welcome! Please feel free to submit a Pull Request. For major changes, please open an issue first to discuss what you would like to change.

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

• Anshul Ganumpally

This application is for research purposes only and should not be used as a substitute for professional medical advice. Always consult with healthcare professionals for medical diagnosis.