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

doi:10.1117/12.2549701

16 March 2020 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

Proceedings Volume 11314, Medical Imaging 2020: Computer-Aided Diagnosis; 113141Q (2020) https://doi.org/10.1117/12.2549701
Event: SPIE Medical Imaging, 2020, Houston, Texas, United States

Abstract

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 automated and accurate detection of P. vivax is difficult due to the low 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 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.

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Citation Download Citation

Feng Yang, Nicolas Quizon, Hang Yu, Kamolrat Silamut, Richard J. Maude, Stefan Jaeger, and Sameer Antani "Cascading YOLO: automated malaria parasite detection for Plasmodium vivax in thin blood smears", Proc. SPIE 11314, Medical Imaging 2020: Computer-Aided Diagnosis, 113141Q (16 March 2020); https://doi.org/10.1117/12.2549701

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