Malaria Detection System
An AI-assisted blood-smear screening prototype
A team-built prototype that connects image-based malaria screening with patient records, role-based dashboards, and downloadable reports. It has not been clinically validated.
The product needed a clear way to move from an uploaded cell image to a model output and an associated patient record, while making the limits of the prototype visible.
The React interface handles doctor, patient, and administrator views. FastAPI loads a Keras model for image inference, while Supabase supports account and data workflows. The application can generate and share PDF reports.
The live prototype demonstrates an end-to-end image analysis and report workflow. It is a student software project and has not been tested or approved for clinical use.
Technical Specs & Highlights
Connected a Keras inference model to FastAPI endpoints.
Built patient and report flows in a React dashboard.
Used Supabase authentication and database services.
Designed the result view to show the model output within a broader record workflow.
Looking for this skill set?
Jayant is open to full-stack engineering roles, internships, and high-impact software challenges.
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