Project Purpose and Goals
👩🏻 Users can:
👩🏼⚕️ Healthcare Professionals can:
- track their symptom in NLP/GPT model calendar.
- get actionable advice on when to seek medical attention.
- educated about tumor types, symptom and lobes from dynmaic website and ML driven analysis.
- facilitate better treatment planning with system-generated insights.
- reduce repetitive questioning and paperwork.
Prototype
What did I build it with?
- ML framework: TensorFlow, PyTorch, Keras, Skikit-Learn
- NLP: GPT model
- Frontend: Tailwind CSS, JavaScript (ReactJS)
- Backend: Firebase
- Libraries: Pandas, Matplotlib, Numpy
- Cloud Platform: Hosted on Vercel
- Collaboration Tools: CI/CD pipelines with GitHub Workflows
- Additional tools: Figma, Docker, Firebase Authentication, Kaggle API
Symptom Assessment and Logging & AI-Generated Reports
- Step1: User inputs their symtpom on detailed symptom tracking calendar
- Step2: It generates various charts based on the specified input time periods.
- Step3: It generates report that summarizes their symptoms and advises whether a user should seek medical attention.
Tumor Detector
- Models were deployed by using hugging face and docker.
- Step1: User can upload their MRI images and get the result in seconds.
- Step2: Tumor Dector Detects tumor size, volume, and location and predicts on growth rate.
- Step3: User can download pdf report that can be shared with their healthcare provider.