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Proceedings Paper

Cascading YOLO: automated malaria parasite detection for Plasmodium vivax in thin blood smears
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Paper 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.

Paper Details

Date Published: 16 March 2020
PDF: 7 pages
Proc. SPIE 11314, Medical Imaging 2020: Computer-Aided Diagnosis, 113141Q (16 March 2020); doi: 10.1117/12.2549701
Show Author Affiliations
Feng Yang, National Institutes of Health (United States)
Nicolas Quizon, National Institutes of Health (United States)
Hang Yu, National Institutes of Health (United States)
Kamolrat Silamut, Mahidol Univ. (Thailand)
Richard J. Maude, Mahidol Univ. (Thailand)
Univ. of Oxford (United Kingdom)
Harvard Univ. (United States)
Stefan Jaeger, National Institutes of Health (United States)
Sameer Antani, National Institutes of Health (United States)

Published in SPIE Proceedings Vol. 11314:
Medical Imaging 2020: Computer-Aided Diagnosis
Horst K. Hahn; Maciej A. Mazurowski, Editor(s)

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