Ensembling Object Detection Models for Robust and Reliable Malaria Parasite Detection in Thin Blood Smear Microscopic Images

Abstract

Malaria is a blood disease caused by the Plasmodium parasite that is transmitted through the bite of female Anopheles mosquitoes. These mosquitoes can cross borders without passports or visas, making malaria a global health concern. To effectively treat malaria, infectious disease specialists must monitor the efficacy of the treatment by counting the number of parasites in a patient’s blood at various time intervals. However, this task is challenging because it involves examining thin or thick blood smear samples under a microscope, which can be tiring to the human eye, particularly when there are many infected patients or when there is a shortage of clinical experts. In such cases, rapid diagnosis is crucial. One approach is to capture microscopic images of blood smear samples using a camera and then employ deep learning-based object detection models to detect and count the infected red blood cells. In this study, state-of-the-art object detection models, including CenterNet, EfficientDet, Faster R-CNN, RetinaNet, and YOLOv8, were explored. The dataset was generated using thin blood smear images in the laboratory. The results revealed that YOLOv8s outperformed the other models, achieving an score of 0.9031 and an mAP@[0.50:0.05:0.95] score of 0.5957. This study also found that various model combinations and ensemble strategies could improve the detection of malaria parasites. Specifically, the weighted boxes fusion ensembling approach achieved an score of 0.9186 and an mAP@[0.50:0.05:0.95] score of 0.6196. In contrast, the non-maximum weighted method achieved an score of 0.9324 and an mAP@[0.50:0.05:0.95] score of 0.6214.

Keywords

• CenterNet
• computer vision
• deep learning
• EfficientDet
• ensemble learning
• faster R-CNN