Cascading YOLO: automated malaria parasite detection for Plasmodium vivax in thin blood smears
Feng Yang, Nicolas Quizon, Hang Yu, Kamolrat Silamut, Richard J. Maude, Stefan Jaeger, Sameer Antani
Abstract
Malaria, caused by <i>Plasmodium</i> parasites, continues to be a major burden on global health. <i>Plasmodium falciparum</i> (<i>P. falciparum</i>) and <i>Plasmodium vivax</i> (<i>P. vivax</i>) 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 automated and accurate detection of <i>P. vivax</i> is difficult due to the low parasitemia levels as compared to <i>P. falciparum</i>. In this work, we develop a rapid and robust diagnosis system for the automated detection of <i>P. vivax</i> parasites using a cascaded YOLO model. This system consists of a YOLOv2 model and a classifier for hardnegative 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.