NLM - Malaria Data
Malaria Project page| Dataset | Number of patients | Annotations | Publications | Links | Format | |
|---|---|---|---|---|---|---|
| Thick Smears | Falciparum | 150 infected patients |
Parasites, 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: https://ieeexplore.ieee.org/document/8846750 ) 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: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7656677/ ) see also Ref. 3 below. |
NLM-Falciparum-Thick-150Patients
IEEE-Falciparum-Thick-150Patients |
3024 x 4032 JPEG |
| Vivax | 150 infected patients |
Parasites, 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: https://www.mdpi.com/2075-4418/11/11/1994 ). 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: https://link.springer.com/chapter/10.1007/978-3-030-32692-0_9 ) |
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, Parasite_Outside_Cell, Dead_Parasite, Gametocyte, White_Blood_Cell, Debris, Stain_Precipitation, Bacteria, Platelet, Air_Bubble, Other, Unclear |
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: https://ieeexplore.ieee.org/document/9244549 ). 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: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6050500/ ) 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: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5907772/ ) 8. Rajaraman S, Jaeger S, Antani SK. Performance evaluation of deep neural ensembles toward malaria parasite detection in thin-blood smear images. PeerJ. (URL: https://doi.org/10.7717/peerj.6977 ). |
Image dataset: NLM-Falciparum&Uninfected-Thin-193Patients Code: GitHub-Code-RBC detection Single cell dataset: NLM-Falciparum-Thin-Cell-Images CSV files for Patient-ID to cell mappings: Parasitized Uninfected Code: Cell-Classification-Thin Other links: TensorFlow Kaggle |
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: https://doi.org/10.1117/12.2549701; https://lhncbc.nlm.nih.gov/LHC-publications/PDF/Final-Paper_SPIE_Feng-Yang_Final.pdf ) | TBD | 3024 x 4032 TIFF | |
Contact: Stefan Jaeger, Feng Yang
National Library of Medicine, National Institutes of Health, Bethesda, MD 20894