A complete automated pipeline using CNNs and gradient boosted trees quantifies malaria parasitemia and identifies Plasmodium species on field-prepared thin blood films with accuracy near clinical usefulness.
Mehanian, et al., ”Computer-Automated Malaria Diagnosis and Quantitation Using Convolutional Neural Networks”, CVPR, 2017
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Fully-automated patient-level malaria assessment on field-prepared thin blood film microscopy images, including Supplementary Information
A complete automated pipeline using CNNs and gradient boosted trees quantifies malaria parasitemia and identifies Plasmodium species on field-prepared thin blood films with accuracy near clinical usefulness.