Introduction

The Brain Tumor Monitoring System is a comprehensive MLOps solution designed to monitor and detect data drift in machine learning models that classify brain tumor images. This system provides real-time monitoring, automated drift detection, and comprehensive reporting capabilities to ensure the reliability and accuracy of medical AI systems.

Problem Statement

In medical AI applications, particularly brain tumor classification, model performance can degrade over time due to:

  • Data Drift: Changes in image characteristics, lighting conditions, or scanning protocols

  • Concept Drift: Evolution in tumor characteristics or classification criteria

  • Model Decay: Gradual degradation of model performance over time

  • Quality Issues: Variations in image quality, artifacts, or preprocessing

Traditional monitoring approaches often fail to detect these issues early, leading to:

  • Reduced Model Accuracy: Degraded performance affecting patient care

  • False Positives/Negatives: Incorrect classifications with serious medical implications

  • Lack of Transparency: Inability to explain model decisions and drift patterns

  • Reactive Response: Late detection of issues requiring emergency model updates

Solution Overview

Our Brain Tumor Monitoring System addresses these challenges through:

Comprehensive Feature Extraction

Automatic extraction of 20+ image features including brightness, contrast, entropy, and tumor-specific characteristics.

Advanced Drift Detection

Statistical analysis comparing current data distributions against reference datasets with customizable thresholds.

Real-time Monitoring

Continuous monitoring of model predictions with immediate alerting for significant drift.

Automated Reporting

HTML reports with visualizations using Evidently AI for stakeholder communication.

Scalable Architecture

Microservices-based design supporting cloud deployment and horizontal scaling.

System Architecture

The system consists of several key components:

Backend API (FastAPI)

RESTful API providing monitoring endpoints, prediction logging, and drift analysis.

Monitoring Engine

Core monitoring logic including feature extraction, drift detection, and report generation.

Database (PostgreSQL)

Persistent storage for prediction logs, feature data, and monitoring metadata.

Frontend Dashboard (React)

Real-time monitoring interface with interactive charts and report viewing.

ML Pipeline Integration

Seamless integration with existing ML training and prediction pipelines.

Key Features

Image Feature Extraction

  • Basic features: width, height, channels, file size

  • Statistical features: brightness, contrast, entropy, skewness, kurtosis

  • Tumor-specific features: detection confidence, area ratios, location data

  • Quality metrics: noise levels, artifact detection

Drift Detection

  • Statistical comparison of reference vs. current data distributions

  • Customizable drift thresholds for different features

  • Clean dataset splitting to avoid overlap

  • Multiple drift detection algorithms

Real-time Monitoring

  • Live dashboard with key metrics

  • Automated alerting for drift detection

  • Historical trend analysis

  • Performance tracking over time

Reporting & Visualization

  • HTML reports with interactive charts

  • Feature distribution comparisons

  • Drift score visualizations

  • Export capabilities for stakeholders

API Integration

  • RESTful endpoints for all monitoring operations

  • Background task processing for non-blocking operations

  • Comprehensive error handling and logging

  • CORS support for frontend integration

Technology Stack

Backend

  • FastAPI: Modern, fast web framework

  • SQLAlchemy: Database ORM

  • PostgreSQL: Primary database

  • Evidently AI: Drift detection and reporting

Frontend

  • React: Modern UI framework

  • TypeScript: Type-safe development

  • Tailwind CSS: Utility-first styling

  • Chart.js: Interactive visualizations

ML & Monitoring

  • OpenCV: Image processing

  • NumPy/Pandas: Data manipulation

  • YOLOv8: Object detection

  • Custom drift detection algorithms

Deployment

  • Docker: Containerization

  • Docker Compose: Local development

  • GitHub Actions: CI/CD automation

  • GCP: Cloud deployment

Use Cases

Medical AI Monitoring

Monitor brain tumor classification models in production environments.

Research & Development

Track model performance during development and validation phases.

Clinical Trials

Monitor AI system performance in clinical trial settings.

Quality Assurance

Ensure consistent model performance across different imaging protocols.

Regulatory Compliance

Maintain audit trails and documentation for regulatory requirements.

Benefits

Early Detection

Identify drift issues before they impact patient care.

Proactive Maintenance

Schedule model updates based on drift patterns rather than reactive fixes.

Transparency

Clear documentation of model behavior and drift patterns.

Scalability

Support for multiple models and deployment environments.

Cost Efficiency

Reduce costs associated with model failures and emergency updates.

Getting Started

To get started with the Brain Tumor Monitoring System:

  1. Installation: Follow the Installation Guide guide

  2. Quick Start: Use the Quick Start Guide tutorial

  3. API Reference: Explore the api/index documentation

For detailed technical information, explore the specific component documentation:

  • monitoring/index - Monitoring system details

  • ml/index - Machine learning pipeline

  • frontend/index - Dashboard interface

  • api/index - API reference