🧠 AI-Powered MRI Brain Tumor Segmentation

A comprehensive deep learning project that transforms medical imaging workflow - from research to production deployment.

Project Overview

This project tackles one of healthcare's most critical challenges: brain tumor diagnosis. I developed an end-to-end AI solution that can segment brain tumors from MRI scans in under 5 minutes - a process that typically takes radiologists 30 minutes to several hours.

The Impact: My best model achieves a 96.34% Dice score, significantly outperforming typical human expert consistency (73-85%) and demonstrating the potential to revolutionize medical imaging workflows.

The Problem I Solved

Healthcare Challenge

Brain tumor segmentation is crucial for diagnosis and treatment planning, but the current manual process has serious limitations:

• Time-Intensive: Radiologists spend 30 minutes to several hours per scan
• Inconsistent Results: Human experts achieve only 73-85% consistency (Dice scores)
• Clinical Bottleneck: Delays in diagnosis and treatment planning
• High Stakes: Glioblastoma has only a 6.9% five-year survival rate - accuracy and speed matter

My Solution

I built a comprehensive AI pipeline that delivers:

• ⚡ 95% faster processing: 5 minutes vs. hours
• 🎯 Superior accuracy: 96.34% consistency vs. 73-85% human
• 🔄 End-to-end automation: From raw MRI to deployable web app
• 🌐 Production-ready: Interactive Streamlit application

Technical Approach & Architecture

Deep Learning Models I implemented and rigorously benchmarked three distinct architectures, each representing different approaches to medical image segmentation: ResNetUNet - Enhanced U-Net with pre-trained ResNet34 backbone

Dice Score: 96.34% Key Insight: Transfer learning dominates

BaselineUNet - Standard U-Net built from scratch

Dice Score: 50.59% Key Insight: Good foundation, limited generalization

TransUNet - Simplified CNN-Transformer hybrid foundation

Dice Score: 6.86%* Key Insight: Architectural complexity challenges

*My TransUNet implementation was deliberately simplified as a stepping stone toward full Vision Transformer integration - the low performance revealed critical insights about skip connections and architectural requirements.

Advanced Data Engineering Pipeline

Dataset: BraTS-Africa (146 patients, 4 MRI modalities each)

• Multi-modal Integration: Stacked T1, T1c, T2, and FLAIR sequences into 4-channel volumes
• Smart Preprocessing: Automated cropping of empty "air" slices, reducing dataset by ~30%
• Normalization Strategy: Min-max scaling across modalities for training stability
• 3D-to-2D Conversion: Generated 10,692 training slices from 146 3D volumes
• Data Integrity: Rigorous 70/15/15 train/validation/test split maintaining patient-level separation

Key Technical Achievements

🏗️ End-to-End Pipeline Development

Built complete workflow from raw medical data to production deployment:

• Data preprocessing and augmentation
• Model training with multiple architectures
• Performance benchmarking and validation
• Web application development and deployment

🧠 Advanced Medical AI Implementation

• Multi-Modal Fusion: Expertly handled 4 MRI sequences (T1, T1c, T2, FLAIR) with strategic channel stacking
• Loss Function Engineering: Designed hybrid BCE + Dice Loss to handle severe class imbalance and optimize for clinical metrics
• Transfer Learning Mastery: Leveraged pre-trained ResNet34 backbone, achieving 46% performance improvement over from-scratch training
• Architecture Analysis: Conducted thorough failure analysis revealing critical insights about skip connections and model convergence

🚀 Production Deployment

Created an interactive web application that allows real-time tumor segmentation:

• User-friendly interface for medical professionals
• Real-time model inference
• Visual comparison of different architectures

Try the Live Demo →

Development Process & Skills Demonstrated

Research & Analysis

• Conducted comprehensive benchmarking of 3 distinct architectures (U-Net variants + Transformer hybrid)
• Performed rigorous quantitative and qualitative analysis of model failures
• Identified key insights: transfer learning superiority, skip connection necessity, generalization challenges
• Analyzed 10,692 2D slices across 146 patients with multi-modal MRI data

Technical Implementation

• Deep Learning: PyTorch, U-Net, ResNet34 transfer learning, custom loss functions
• Medical Data Processing: NIfTI format handling, 3D-to-2D conversion, multi-modal fusion
• Performance Engineering: BCE+Dice hybrid loss, class imbalance handling, training optimization
• Model Analysis: Comprehensive failure analysis, architectural insights, convergence studies

Project Management

• Structured development using Jupyter notebooks for reproducibility
• Clear documentation and code organization
• Version control and collaborative development practices

Results & Impact

Quantitative Results

• 96.34% Dice Score: ResNetUNet achieves state-of-the-art performance
• 46% improvement: Transfer learning vs. from-scratch training (96.34% vs 50.59%)
• 10,692 samples processed: Successfully scaled from 146 3D volumes
• Expert-level consistency: Exceeds human radiologist agreement (73-85%) by 10-20%

Key Technical Discoveries

Transfer Learning Dominance: Pre-trained ResNet34 backbone dramatically outperformed from-scratch training, demonstrating the power of leveraging established visual features.

Architectural Insights: My TransUNet failure analysis revealed critical requirements for medical segmentation - specifically the necessity of skip connections for preserving spatial detail during decoder reconstruction.

Loss Function Engineering: The hybrid BCE+Dice loss successfully balanced pixel-level accuracy with structural similarity, directly optimizing for the clinical evaluation metric.

Technical Learning Outcomes

• Mastered medical AI domain-specific challenges
• Gained expertise in U-Net and advanced CNN architectures
• Developed skills in 3D medical data processing
• Created production-ready ML applications

Potential Real-World Impact

This project demonstrates how AI can transform healthcare delivery:

• Clinical Efficiency: Dramatically reduces radiologist workload
• Diagnostic Consistency: Eliminates human variability in critical diagnoses
• Accessibility: Makes expert-level analysis available globally
• Treatment Planning: Provides precise tumor boundaries for radiation therapy