About This Architecture

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.

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How do you build a complete machine learning pipeline for medical image analysis and clinical decision support?

This kidney stone detection pipeline demonstrates the full workflow: patient uploads CT/ultrasound images via Flask/Django web interface, OpenCV normalizes and augments images to 224x224 tensors, a TensorFlow CNN with VGG16 transfer learning generates confidence scores, and a rule-based decision engine combines model output with symptom data to classify stone presence and severity, outputting reco