NLM - Malaria Data

Malaria Screener Project PageMalaria Screener Downloads Page
Dataset Number of patients Annotations Publications Links Format
Thick Smears Falciparum 150
infected patients
White blood cells
1. Yang F, Poostchi M, Yu H, Zhou Z, Silamut K, Yu J,Maude RJ, Jaeger S, Antani S. Deep Learning for Smartphone-Based Malaria Parasite Detection in Thick Blood Smears. IEEE J Biomed Health Inform. 2020 May;24(5):1427-1438. ( URL: )

2. Yu H, Yang F, Rajaraman S, Ersoy I, Moallem G, Poostchi M, Palaniappan K, Antani S, Maude RJ, Jaeger S. Malaria Screener: a smartphone application for automated malaria screening. BMC Infect Dis. 2020 Nov 11;20(1):825. (URL: )

see also Ref. 3 below.

3024 x 4032 JPEG
Vivax 150
infected patients
White blood cells
3. Kassim Y M, Yang F, Yu H, Maude R J, Jaeger S. Diagnosing Malaria Patients with Plasmodium falciparum and vivax Using Deep Learning for Thick Smear Images. Diagnostic, 11(11):1994, 2021. (URL: ).

4. Yang F, Yu H, Silamut K, Maude R, Jaeger S, Antani SK Smartphone-Supported Malaria Diagnosis Based on Deep Learning. Proceedings of 10th Workshop on Machine Learning in Medical Imaging (MLMI 2019)inconjunctionwithMICCAI,Shenzhen, China, Oct 13-17, 2019. (URL: )
NLM-Vivax-Thick-150Patients 3024 x 4032 JPEG
Uninfected 50 uninfected patients White blood cells NLM-Uninfected-Thick-50Patients 3024 x 4032 JPEG
Thin Smears Falciparum and uninfected patients 193 patients (148 infected + 45 uninfected patients) Uninfected and
infected RBCs,
Bacteria, Platelet,
Air_Bubble, Other,
5. Kassim YM, Palaniappan K, Yang F, Poostchi M, Palaniappan N, Maude RJ, Antani S, Jaeger S. Clustering-Based Dual Deep Learning Architecture for Detecting Red Blood Cells in Malaria Diagnostic Smears. IEEE J Biomed Health Inform. 2021 May;25(5):1735-1746. (URL: ).

6. Rajaraman S, Silamut K, Hossain MA, Ersoy I, Maude RJ, Jaeger S, Thoma GR, Antani SK. Understanding the learned behavior of customized convolutional neural networks toward malaria parasite detection in thin blood smear images. J Med Imaging (Bellingham). 2018 Jul;5(3):034501. (URL: )

7. Rajaraman S, Antani SK, Poostchi Mohammadabadi M, Silamut K, Hossain MA, Maude RJ, Jaeger S, Thoma GR. Pre-trained convolutional neural networks as feature extractors toward improved malaria parasite detection in thin blood smear images. PeerJ. 2018 Apr 16;6:e4568. (URL: )

8. Rajaraman S, Jaeger S, Antani SK. Performance evaluation of deep neural ensembles toward malaria parasite detection in thin-blood smear images. PeerJ. (URL: ).
Image dataset:

GitHub-Code-RBC detection

Single cell dataset:

CSV files for Patient-ID to cell mappings:


Other links:
2988 x 5312 JPEG
Vivax 171
infected patients
Parasites 9. Yang F, Quizon N, Silamut K, Maude RJ, Jaeger S, Antani SK. 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); (URL:; ) TBD 3024 x 4032 TIFF

Contact: Stefan Jaeger, Feng Yang
National Library of Medicine, National Institutes of Health, Bethesda, MD 20894