# Fully-automated patient-level malaria assessment on field-prepared thin blood film microscopy images, including Supplementary Information

arXiv: https://arxiv.org/abs/1908.01901 · HTML: https://mlsanity.com/paper/1908.01901/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Aug 2019.

## Built on by (most cited first, 4 of 4)

- [Metrics to guide development of machine learning algorithms for malaria diagnosis](https://mlsanity.com/paper/2209.06947/) (2022)
- [Assessing Generalization Capabilities of Malaria Diagnostic Models from Thin Blood Smears](https://mlsanity.com/paper/2408.08792/) (2024)
- [Driving down Poisson error can offset classification error in clinical tasks](https://mlsanity.com/paper/2405.06065/) (2024)
- [SGMCE: Segment-Grounded Morphological Concept Explanation for Malaria Parasite Species Identification in Thick Blood Smears](https://mlsanity.com/paper/2607.16324/) (2026)

## Abstract (arXiv)

Malaria is a life-threatening disease affecting millions. Microscopy-based assessment of thin blood films is a standard method to (i) determine malaria species and (ii) quantitate high-parasitemia infections. Full automation of malaria microscopy by machine learning (ML) is a challenging task because field-prepared slides vary widely in quality and presentation, and artifacts often heavily outnumber relatively rare parasites. In this work, we describe a complete, fully-automated framework for thin film malaria analysis that applies ML methods, including convolutional neural nets (CNNs), trained on a large and diverse dataset of field-prepared thin blood films. Quantitation and species identification results are close to sufficiently accurate for the concrete needs of drug resistance monitoring and clinical use-cases on field-prepared samples. We focus our methods and our performance metrics on the field use-case requirements. We discuss key issues and important metrics for the application of ML methods to malaria microscopy.