End-to-End AI-Powered Medical Imaging Platform
This project demonstrates a production-style AI system for chest X-ray image classification. Rather than focusing only on model training, the system delivers a complete workflow: model inference, web interface, and cloud-based data storage.
The system classifies radiographs into four categories:
The application enables users to:
Medical imaging workflows require both accuracy and efficiency. During high-demand periods, radiology teams must process large volumes of cases while maintaining clinical standards.
This system illustrates how AI can function as a decision-support layer — accelerating preliminary image analysis while keeping human oversight central to the workflow.
The system leverages transfer learning using a pre-trained DenseNet-161 architecture, adapted for multi-class medical image classification.
The focus of this project is not only model accuracy, but reliable inference and integration into a usable application.
Full AI Workflow:
Data → Training → Inference → Web Interface → Cloud Storage
Click to watch system demonstration