I developed a deep learning system that analyzes brain MRI scans to detect tumors with 94% accuracy, combining computer vision with healthcare applications.

Technical Implementation

• Built a CNN from Scratch

  Designed a convolutional neural network in PyTorch to process medical images
  Optimized layers and hyperparameters through iterative testing
  

• Data & Performance

  Trained on real-world MRI datasets (healthy vs. tumor scans)
  Achieved 94% classification accuracy through:
  Careful layer architecture tuning
  Strategic data augmentation
  

Key Challenges & Breakthroughs

• Medical Data Nuances

  Learned to work with specialized DICOM/medical image formats
  Addressed dataset imbalances through augmentation
  

• Model Optimization

  Experimented with different kernel sizes and pooling layers
  Balanced precision and recall for clinical relevance
  

Why This Matters

Proved I can apply deep learning to real-world problems Gained experience with:
Computer vision for medical imaging, PyTorch's full pipeline (from data loading to prediction), Ethical considerations in medical AI. Building this wasn't just about accuracy percentages - it was about understanding how AI could one day assist doctors in life-saving work.

View GitHub repo