TumorX

TumorX: AI-Powered, Decentralized, and Life-Saving Brain Tumor Diagnostics

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TumorX Project Overview

Inspiration

Brain tumor diagnosis in India is often a race against time — and the system is falling behind. A severe shortage of radiologists, overwhelming patient loads, and limited diagnostic infrastructure — especially in rural and semi-urban regions — mean thousands of patients experience delayed detection, late interventions, escalating treatment costs, and reduced survival chances. We wanted to create a solution that could provide fast, reliable, and accessible tumor detection and analysis, bridging the gap between patients and expert radiologists.

What it does

TumorX is an AI-powered diagnostic assistant that:

  • Detects the presence of brain tumors (Tumor / No Tumor).
  • Classifies tumor types (Glioma, Meningioma, Pituitary).
  • Segments tumors to calculate volume, largest size, and centroid coordinates.
  • Provides a priority score for urgency-based triaging.
  • Offers dual interfaces:
    • Patient view for easy uploads
    • Doctor view for analytics and planning

How we built it

  • Built a 3D U-Net deep learning model for tumor segmentation.
  • Leveraged federated learning for decentralized, privacy-preserving model training across multiple hospitals.
  • Developed interfaces for both patients and clinicians using Python-based dashboards.

Challenges we ran into

  • Limited labeled datasets for training high-accuracy models.
  • Handling 3D MRI volumes required significant computational resources.
  • Ensuring data privacy while aggregating insights from multiple centers.
  • Balancing model accuracy with inference speed for real-time diagnostics.

Accomplishments that we're proud of

  • Built a working AI-powered tumor segmentation and classification system.
  • Successfully implemented tumor volume, size, and location calculations from MRI scans.
  • Demonstrated privacy-preserving collaborative learning with federated learning.
  • Created a user-friendly dual interface for both patients and doctors.

What we learned

  • Deep learning can significantly accelerate and improve diagnostic accuracy in medical imaging.
  • Data preprocessing and augmentation are critical for 3D medical image segmentation.
  • Federated learning enables secure collaboration without compromising patient privacy.
  • Effective visualization of tumor metrics helps clinicians make faster, informed decisions.

What's next for TumorX

  • Expand support for more MRI modalities and larger datasets to improve accuracy.
  • Integrate real-time deployment in hospitals with secure federated updates.
  • Add predictive analytics to forecast tumor growth and treatment outcomes.
  • Explore mobile and cloud integration for wider accessibility in remote areas.
  • Implement Quantum Key Distribution (QKD) for quantum-safe data security.
  • Leverage blockchain and Web3 technologies for transparent, secure, and decentralized healthcare networks.

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