An Integrated Three-Tier Microservice Architecture for Deploying State-of-the-Art Ocular Cancer Detection Models

This paper details the design and implementation of a scalable three-tier microservice system architecture optimized for the deployment of deep learning models in real-time ocular cancer screening. The platform decouples the presentation layer (React), the data and routing layer (Node.js/Ex-press API Gateway), and the specialized machine learning inference service (Python/FastAPI). The system supports a robust multi-stage diagnostic workflow, currently implemented using a ResNet50based transfer learning model trained across two phases to achieve empirically verified perfect classification on the test set. The model, which targets Retinoblastoma (RB) [7], achieved an Accuracy, Precision, and Recall of 100% in its independent test set. This microservice architecture has been demonstrated to be critical for the effective and scalable integration of high performance AI tools into practical clinical environments, especially low-resource settings, by providing robust data persistence, dedicated user management, built-in system traceability, and seamless diagnostic request routing.