About This Architecture

ResNet34-UNet with CBAM attention mechanism powers this end-to-end CT image segmentation architecture, processing radiological scans from upload through AI-driven mask generation. User requests flow through WAF and API Gateway to a load-balanced backend that orchestrates image preprocessing, model inference, and result processing across three tiers. The system persists raw CT images in object storage and segmentation results in a database, with integrated monitoring and logging for production reliability. Fork this diagram on Diagrams.so to customize model components, adjust tier scaling, or adapt for alternative medical imaging modalities like MRI or ultrasound. The CBAM module enhances spatial and channel attention, critical for precise organ and lesion delineation in clinical workflows.

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How do you architect a production CT image segmentation system using ResNet34-UNet with CBAM?

This diagram shows a three-tier architecture where CT images flow through a secure API gateway and load balancer to a backend service that orchestrates preprocessing, ResNet34-UNet+CBAM inference, and mask generation. Raw images are stored in object storage while segmentation results persist in a database, with monitoring and logging ensuring clinical-grade reliability.