This page hosts a repository of P. vivax and P. falciparum images in both thin and thick blood smears from the Malaria Screener research activity.
To reduce the burden for microscopists in resource-constrained regions and improve diagnostic accuracy, researchers at the Lister Hill National Center for Biomedical Communications (LHNCBC), part of National Library of Medicine (NLM), have developed a mobile application, called “Malaria Screener”, which runs on a standard Android smartphone attached to a conventional light microscope. The smartphone's built-in camera acquired images of slides for each microscopic field of view. The images were manually annotated by an expert slide reader at the Mahidol-Oxford Tropical Medicine Research Unit in Bangkok, Thailand. The de-identified images and annotations are archived at NLM (IRB#12972).
The dataset includes five main parts:
Giemsa-stained thick blood smear slides from 150 P. falciparum-infected patients were collected and photographed at Chittagong Medical College Hospital, Bangladesh. We have developed the first deep learning method that can detect P. falciparum parasites in thick blood smear images and can run on smartphones, which consists of two modules: an intensity-based Iterative Global Minimum Screening (IGMS) module for parasite candidate screening and a customized CNN classifier for final classification. The data was published along with the following publication:
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 )
Giemsa-stained thick blood smear slides from 150 P. vivax-infected patients and 50 uninfected patients were collected and photographed at Chittagong Medical College Hospital, Bangladesh. Based on a dataset of 350 malaria patients, we proposed PlasmodiumVF-Net to diagnose a patients as uninfected, P. vivax-infected, or P. falciparum-infected. The data was published along with the publication:
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 )
Giemsa-stained thin blood smear slides from 148 P. falciparum-infected, and 45 uninfected patients were collected and photographed at Chittagong Medical College Hospital, Bangladesh. We proposed RBCNet that consists of a U-Net first stage for cell-cluster or super pixel segmentation, followed by a second refinement stage Faster R-CNN for detecting small cell objects within the connected component clusters. The corresponding publication is:
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 )
Giemsa-stained thin blood smear slides from 171 P. vivax-infected patients were collected and photographed in Bangkok, Thailand. We developed 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 hard-negative mining; see the following publication:
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 )
We acquired cell images from 150 P. falciparum-infected and 50 uninfected patients in Giemsa-stained thin blood smears that were collected and photographed at Chittagong Medical College Hospital, Bangladesh. The dataset contains a total of 27,558 cell images with equal instances of parasitized and uninfected cells. An instance of how the patient-ID is encoded into the cell name is shown herewith: “P1” denotes the patient-ID for the cell labeled “C33P1thinF_IMG_20150619_114756a_cell_179.png”. We have also included the CSV files containing the Patient-ID to cell mappings for the parasitized and uninfected classes. The CSV file for the parasitized class contains 151 patient-ID entries. The slide images for the parasitized patient-ID “C47P8thinOriginal” are read from two different microscope models (Olympus and Motif). The CSV file for the uninfected class contains 201 entries since the normal cells from the infected patients’ slides are also in the normal cell category (151+50 = 201). Experiments with the data were reported in the following paper (PeerJ6:e4568):
Rajaraman S, Antani SK, Poostchi M, Silamut K, Hossain MA, Maude, RJ, Jaeger S, Thoma GR. (2018) Pre-trained convolutional neural networks as feature extractors toward improved Malaria parasite detection in thin blood smear images. (URL: https://doi.org/10.7717/peerj.4568 )
Malaria Screener datasheet Details of datasets and download links
Malaria Screener App Download our smartphone-based software "Malaria Screener"
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