Cascading YOLO: Automated Malaria Parasite Detection for Plasmodium Vivax in Thin Blood Smears

摘要

Malaria, caused by Plasmodium parasites, continues to be a major burden on global health. Plasmodium falciparum (P. falciparum) and Plasmodium vivax (P. vivax) pose the greatest health threat among the five malaria species. Microscopy examination is considered as the gold standard for malaria diagnosis, but it requires a significant amount of time and expertise. In particular, the detection of P. vivax is more difficult due to the lower parasitemia levels as compared to P. falciparum. In this work, we develop a rapid and robust diagnosis system for the automated detection of P. vivax parasites using a cascaded YOLO model. This system consists of a YOLOv2 model and a classifier for hard-negative mining. Results from 2567 thin blood smear images of 171 patients show the cascaded YOLO model improves the mean average precision about 8% compared to the conventional YOLOv2 model.