Abstract
BackgroundLight 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.ResultsWe 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.ConclusionMalaria 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.
Highlights
Light microscopy is often used for malaria diagnosis in the field
It allows the user to continue with other things while the upload tasks proceed in the Results A fast and effective mobile app is developed as a lightweight solution to automated malaria light microscopy
The workflow proceeds in six steps as follows, with each step corresponding to a panel in (Fig. 5 (a)): (1) Once the smartphone is setup on top of the microscope, using an adapter, the user can start a session from the main page of the app
Summary
Light microscopy is often used for malaria diagnosis in the field. It is time-consuming and quality of the results depends heavily on the skill of microscopists. Microscopic examination of stained blood smears is still considered the gold standard for malaria diagnosis [1, 2]. 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. The universal smartphone microscope adapter we use costs less than $100 (from telescopeadapters.com, model: USPA2). The low-cost design and easy-to-use interface give the system great potential to assist malaria diagnosis in resource-limited areas. The modular architecture allows it to be adapted by fellow researchers to advance their study
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