A COCO-Formatted Instance-Level Dataset for Plasmodium Falciparum Detection in Giemsa-Stained Blood Smears
This work addresses the scarcity of annotated datasets for malaria detection in developing countries, but it is incremental as it refines existing data rather than introducing new methods.
The authors tackled the problem of limited datasets for automated malaria diagnosis by creating an enhanced version of the NIH malaria dataset with detailed COCO-formatted bounding box annotations, and validated it by training a Faster R-CNN model that achieved an F1 score of up to 0.88 for infected cell detection.
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