Transparent, step-by-step AI

Automated malaria detection with lab-grade clarity

Upload thick and thin smear images to classify infection, count parasites, and identify Plasmodium species. Every stage is explainable, timestamped, and confidence-weighted.

~4s inference
Calibrated confidences
Research only
Live inference preview
v2.1 pipeline
Parasite count
42
High severity
Clinical Risk Level
High Risk
Requires immediate attention
Thin smear species detection
Plasmodium falciparum · High risk
Under 5 seconds per image
Built for real labs

Why teams pick MalariaVision

Lab-Grade Accuracy

Trained on 50,000+ expert-annotated blood smear images with 98.5% detection accuracy.

Species Identification

Accurately identifies P. falciparum, vivax, ovale, and malariae with risk assessment.

Fast Processing

Complete 3-step analysis in under 10 seconds with GPU-accelerated inference.

3-Step Workflow

How it works

Our AI pipeline processes blood smear images through three sequential analysis stages.

Step 1

Infection Detection

Upload thick smear image to determine if malaria parasites are present.

Step 2

Parasite Counting

Count parasites and assess infection severity (Low/Medium/High).

Step 3

Species Identification

Upload thin smear to identify Plasmodium species and risk level.