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Computer Visionadvanced

Kidney Tumor Identification System

The Kidney Tumor Identification System is a state-of-the-art Deep Learning application designed to assist medical professionals in the early detection and classification of kidney tumors from CT scan images. By leveraging Convolutional Neural Networks (CNNs) and MLOps best practices, this system provides accurate, automated diagnostics to reduce the workload on radiologists and improve patient outcomes.

18 lectures

What You Will Learn

Master the end-to-end Deep Learning lifecycle from data ingestion to deployment.
Learn to write modular, production-grade Python code for Computer Vision projects.
Understand MLOps principles using DVC for pipeline orchestration and MLflow for experiment tracking.
Gain hands-on experience with Azure and AWS Cloud services for containerized deployment.
Implement CI/CD pipelines using GitHub Actions for automated build and deploy.
Build and deploy scalable APIs using Flask and Docker to serve model predictions.

System Architecture

Kidney Tumor Identification System Architecture Diagram

High-level architecture overview of the Kidney Tumor Identification System .

What You'll Build

  • A robust "Kidney Tumor Identification System" capable of accurately classifying kidney CT scans as "Tumor" or "Normal".
  • An automated Training Pipeline that handles data ingestion, base model preparation (VGG16), training, and evaluation.
  • A Prediction Pipeline exposed via a Flask web application for real-time user predictions.
  • A fully automated deployment workflow that builds Docker images and deploys them to AWS & Azure
Kidney Tumor Identification System
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