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Chest Disease Identification

This project is an end-to-end Machine Learning application designed to classify chest diseases from CT scan images. It demonstrates a complete MLOps lifecycle, incorporating experiment tracking, pipeline orchestration, model versioning, and automated deployment using CI/CD pipelines. The solution leverages Deep Learning (CNNs) for classification and provides a web interface for user interaction.

18 lectures

What You Will Learn

Master the end-to-end Machine Learning lifecycle from data ingestion to deployment.
Learn to write modular, production-grade Python code for ML projects using Deep Learning (CNNs).
Understand MLOps principles including automated pipelines (DVC) and experiment tracking (MLflow).
Gain hands-on experience with AWS Cloud services (EC2, ECR, IAM).
Implement CI/CD pipelines using GitHub Actions for automated deployment.
Learn how to use VGG16 Transfer Learning for image classification.
Build and deploy a Flask-based web application.

System Architecture

Chest Disease Identification Architecture Diagram

High-level architecture overview of the Chest Disease Identification .

What You'll Build

  • A robust Chest Disease Classification System capable of identifying cases from CT scans.
  • An automated Training Pipeline that handles data ingestion, model preparation, training, and evaluation.
  • A Prediction Pipeline exposed via a Flask web user interface for real-time analysis.
  • A fully automated deployment workflow that builds Docker images and deploys them to AWS EC2 whenever code is pushed to GitHub.
Chest Disease Identification
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