Malaria Screener: a smartphone application for automated malaria screening
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Malaria Screener: a smartphone application for automated malaria screening Hang Yu1, Feng Yang1, Sivaramakrishnan Rajaraman1, Ilker Ersoy2, Golnaz Moallem1,3, Mahdieh Poostchi1, Kannappan Palaniappan4, Sameer Antani1, Richard J. Maude5,6,7 and Stefan Jaeger1*
Abstract Background: Light microscopy is often used for malaria diagnosis in the field. However, it is time-consuming and quality of the results depends heavily on the skill of microscopists. Automating malaria light microscopy is a promising solution, but it still remains a challenge and an active area of research. Current tools are often expensive and involve sophisticated hardware components, which makes it hard to deploy them in resource-limited areas. Results: We designed an Android mobile application called Malaria Screener, which makes smartphones an affordable yet effective solution for automated malaria light microscopy. The mobile app utilizes high-resolution cameras and computing power of modern smartphones to screen both thin and thick blood smear images for P. falciparum parasites. Malaria Screener combines image acquisition, smear image analysis, and result visualization in its slide screening process, and is equipped with a database to provide easy access to the acquired data. Conclusion: Malaria Screener makes the screening process faster, more consistent, and less dependent on human expertise. The app is modular, allowing other research groups to integrate their methods and models for image processing and machine learning, while acquiring and analyzing their data. Keywords: Automated light microscopy, Smartphone application, Malaria, Machine learning, Convolutional neural network
Background Microscopic examination of stained blood smears is still considered the gold standard for malaria diagnosis [1, 2]. It offers the ability to characterize parasite species, quantify parasite density, and assess the effectiveness of antimalarial treatment. However, regions that are suffering from the disease are often lacking in well-trained personnel that can perform high-quality microscopy examination due to the high costs to train such experts [3, 4]. Besides, the examination process can be very time-consuming and error-prone. To address these issues, there have been attempts to automate both image acquisition and image analysis for * Correspondence: [email protected] 1 Lister Hill National Center for Biomedical Communications, National Library of Medicine, National Institutes of Health, Bethesda, MD 20894, USA Full list of author information is available at the end of the article
the microscopic examination of blood smears. Gopakumar, G.P. et al. [5] proposed a custom-built portable slide scanner that automatically collects and analyzes focus stacks of blood smear images. Muthumbi, A. et al. [6] proposed a system that adds a programmable LED array to the standard microscope, and uses a large-fieldof-view, low-resolution objective lens to capture thousands of cells in one snapshot. While these methods show great potential, the
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