Kidney stone detection pipeline integrates medical imaging with CNN-based ML inference and rule-based decision logic to deliver diagnostic recommendations. Patient data flows from CT/ultrasound uploads through Flask/Django web interface, OpenCV preprocessing (resize, normalization, augmentation), TensorFlow/Keras CNN model with VGG16 transfer learning, and a decision engine combining confidence scores with symptom weighting. The system classifies stone presence and severity, then outputs results via dashboard with medical advice and PDF reports. This architecture demonstrates best practices for clinical decision support: separating image processing, model inference, and business logic into distinct stages. Fork this diagram to customize thresholds, add DICOM parsing, integrate EHR systems, or deploy on cloud platforms like AWS SageMaker or Azure ML.