Malaria Screener: a smartphone application for automated malaria screening

• Main Authors: Hang Yu, Feng Yang, Sivaramakrishnan Rajaraman, Ilker Ersoy, Golnaz Moallem, Mahdieh Poostchi, Kannappan Palaniappan, Sameer Antani, Richard J. Maude, Stefan Jaeger
• Format: Article
• Language: English
• Published: BMC 2020-11-01
• Series: BMC Infectious Diseases
• Subjects: Automated light microscopy; Smartphone application; Malaria; Machine learning; Convolutional neural network
• Online Access: http://link.springer.com/article/10.1186/s12879-020-05453-1

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.