LungLens is a medical chest X-ray analysis and education tool designed to help people better understand imaging results in plain language. The focus is health literacy: helping users ask better questions, not replacing professional care.
The machine learning model behind LungLens was developed as part of an MSc group project at CUHK Chinese University Hong Kong.
I then built this web application independently so the tool could be freely accessible to everyone in a clean, easy-to-use format.
Engineers, researchers, and builders who trained the models and shaped the LungLens experience.
Full-Stack Developer & Architect
Designed the system architecture and developed the frontend web application and backend API integration. Trained, evaluated, and deployed Model 3 (DenseNet-121) with integrated Grad-CAM visual interpretability.
Vision AI Researcher
Developed and optimized two computer vision pipelines for the ensemble system: Model 1 (ResNet-50) for primary feature classification and Model 4 (Swin Transformer Tiny) for advanced pattern recognition.
Vision AI Researcher & Clinical Logic
Developed and trained Model 2 (ResNet-152V2). Designed the conditional user intake questionnaire to capture patient context and supply structured clinical inputs to the LLM diagnostic module.
Vision AI Researcher
Conducted data analysis on medical reporting standards and deploying Model 5 (DenseNet-121) to scale our diagnostic ensemble.
Code Contributor
Provided assistance with code enhancements and repository maintenance.
• This tool is for educational and research purposes only.
• It is NOT a substitute for professional medical diagnosis.
• Always consult a qualified healthcare professional.
• Model: PyTorch, trained on [dataset name, e.g. NIH ChestX-ray14]
• Frontend: Next.js, Tailwind CSS
• Deployment: [Railway / Cloud Run / etc.]