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Computer Vision
Malaria Detection
97% accuracy malaria classifier — FastAI transfer learning
Binary image classifier distinguishing parasitised from uninfected blood cells using FastAI's transfer learning pipeline on a pretrained ResNet. Achieves 97% accuracy on the NIH cell image dataset with minimal labelled data.
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How it was built
Malaria Detection, from first question to shipped.
StatusShippedall stages complete
Stages4 / 4frame to ship
Headline97%accuracy on the NIH cell dataset
Frame Passed
- Tell parasitised blood cells from uninfected ones
- Minimal labelled data
Build Passed
- FastAI transfer-learning pipeline
- Pretrained ResNet
Validate Passed
- 97% accuracy on the NIH cell image dataset
Ship Passed
- Open-source repo on GitHub