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.
Systems Optimization & Presentation Specialist
Handled backend performance tuning, code quality improvements, and repository maintenance using AI-assisted engineering tools. Co-authored reporting templates and led the final presentation design.
Vision AI Researcher
Conducted data analysis on medical reporting standards and deploying Model 5 (DenseNet-121) to scale our diagnostic ensemble.
• 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.]