# Rapid and accurate recognition of erythrocytic stage parasites of Plasmodium falciparum via a deep learning-based YOLOv3 platform

## TL;DR

A deep learning tool called YOLOv3 was developed to accurately detect malaria parasites in blood samples, offering a promising solution for early diagnosis in areas with limited resources.

## Contribution

A novel AI-assisted diagnostic tool using YOLOv3 for rapid and accurate detection of Plasmodium falciparum in blood smears is introduced.

## Key findings

- The YOLOv3 model achieved an overall recognition accuracy of 94.41% in detecting Plasmodium falciparum-infected red blood cells.
- The model had a false negative rate of 1.68% and a false positive rate of 3.91%.
- The AI-assisted tool shows robust efficiency and potential for malaria control in resource-limited settings.

## Abstract

Malaria remains a fatal global infectious disease, with the erythrocytic stage of Plasmodium falciparum being its main pathogenic phase. Early diagnosis is critical for effective treatment. This study developed and evaluated an artificial intelligence-assisted diagnosis (AI-assisted diagnostic) tool for malaria parasites.

The peripheral blood samples of malaria patients were collected. Thin blood film smear were prepared, stained and examined by microscopic. After manual confirmation and validation with qPCR, the images of infected red blood cells (iRBCs) of P. falciparum were captured. Using a sliding window method, each original image was cropped into 20 small images (518 × 486 pixels). Selected iRBCs were classified, and P. falciparum was detected using the YOLOv3 deep learning-based object detection algorithm.

A total of 262 images were tested. The YOLOv3 model detected 358 P. falciparum-containing iRBCs, with a false negative rate of 1.68% (6 missed iRBCs) and false positive rate of 3.91% (14 misreported iRBCs), yielding an overall recognition accuracy of 94.41%.

The developed AI-assisted diagnostic tool exhibits robust efficiency and accuracy in Plasmodium falciparum recognition in clinical thin blood smears. It provides a feasible technical support for malaria control in resource-limited settings.