Ensembling Object Detection Models for Robust and Reliable Malaria Parasite Detection in Thin Blood Smear Microscopic Images
Emre Özbılge, Emrah Güler, Ebru Ozbılge
Abstract
Malaria is a blood disease caused by the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Plasmodium</i> 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 and 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 [email protected] 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 malarial parasites. Specifically, the weighted boxes fusion ensembling approach achieved an [email protected] score of 0.9186 and an mAP@[0.50:0.05:0.95] score of 0.6196. On the other hand, the non-maximum weighted method achieved an [email protected] score of 0.9324 and an mAP@[0.50:0.05:0.95] score of 0.6214.