Medical image analysis represents one of the most impactful applications of computer vision in healthcare. In this detailed case study, I'll walk you through building a complete knee osteoarthritis classification system that achieved 95% validation accuracy on the OAI dataset. We'll start with data preprocessing techniques specific to medical imaging, explore different CNN architectures including EfficientNetB5, and implement proper data augmentation strategies. The tutorial covers building a Flask REST API for model inference, creating a Flutter mobile app for real-time diagnostics, and ensuring HIPAA compliance in medical AI applications. I'll share the challenges faced during deployment, including handling edge cases, managing false positives, and providing clinically meaningful confidence scores. This system now processes over 200 diagnoses daily in a real healthcare setting.