A COCO-Formatted Instance-Level Dataset for Plasmodium Falciparum Detection in Giemsa-Stained Blood Smears

TL;DR

This paper enhances a malaria dataset with detailed COCO-format annotations, enabling improved deep learning detection of infected blood cells, and demonstrates high accuracy in automated malaria diagnosis.

Contribution

The work provides an improved, richly annotated dataset for malaria detection, facilitating better training of deep learning models for automated diagnosis.

Findings

F1 score of up to 0.88 for infected cell detection

Annotated dataset supports robust deep learning training

Automated annotation refinement improves data quality

Abstract

Accurate detection of Plasmodium falciparum in Giemsa-stained blood smears is an essential component of reliable malaria diagnosis, especially in developing countries. Deep learning-based object detection methods have demonstrated strong potential for automated Malaria diagnosis, but their adoption is limited by the scarcity of datasets with detailed instance-level annotations. In this work, we present an enhanced version of the publicly available NIH malaria dataset, with detailed bounding box annotations in COCO format to support object detection training. We validated the revised annotations by training a Faster R-CNN model to detect infected and non-infected red blood cells, as well as white blood cells. Cross-validation on the original dataset yielded F1 scores of up to 0.88 for infected cell detection. These results underscore the importance of annotation volume and consistency, and demonstrate that automated annotation refinement combined with targeted manual correction can produce training data of sufficient quality for robust detection performance. The updated annotations set is publicly available via Zenodo: https://doi.org/10.5281/zenodo.17514694