Impact on SDG 3.4: Saving Lives through Early Detection

Bridging diagnostic gaps in rural clinics (currently <15% imaging coverage).
Advancing SDG 3.4: Early detection could help save over 600,000 lives annually.
Empowering non-specialists: AI helps community health workers participate in diagnosis and triage.

The 'FinalYearProject' repository is designed to address these critical issues by leveraging cutting-edge technology:

Backend runs a DenseNet121 model trained on 16,000+ images for accurate, offline-ready inference.
💡 No-Code Azure Option: For clinics with limited technical capacity, 800 pre-labeled lung images were trained on Azure Custom Vision for an easy plug-and-play interface.
🤖 AI Chatbot Assistant: Integrated with Azure OpenAI to explain results, answer lung-health questions, and guide users through uploads.
🔐 Secure Viewer Access: Azure Custom Vision access managed via viewer lists—no exposed secrets.

🎯 Uniqueness

Dual Training Paths:

A powerful deep learning model (DenseNet121) trained locally on 16K lung images.
A parallel no-code model trained on 800 curated samples using Azure Custom Vision for accessible cloud-based use.

Both modalities are integrated into a single platform, enhancing accessibility and empowering non-specialist healthcare providers.