Detecting Malaria Cells with Plasmo-D Expert System Developed on Android and Computer Vision

Separation between infected and uninfected cells during diagnosing malaria parasites plasmodium is difficult, time-consuming, and expensive. However, this article presented a report on a developed expert system called Plasmodium Detector(Plasmo-D), capable of differentiating plasmodium-infected and uninfected cells from malaria-infected patients. Plasmo-D was built on an Android application, with an information menu, splash, and classification screen, including an image recognition system that worked with computer vision. 27,528 cell images were collected online from the Data Library of the United States National Library of Medicine, containing infected and uninfected cells for training. No cell images were used as control. Plasmo-D fabrication and testing were conducted at the Instrumentation Laboratory, Department of Systems Engineering, University of Lagos, Nigeria. Studied parameters included cell images, backgrounds, visual style, size, type, lighting, and camera angle. Trained models were exported into an Android application through Java programming language and user interface through Android XML (Extensible Markup Language). Trained data results indicated that 99.8% desired level of accuracy was obtained after cell images were fed into the computer vision application programming interface. The trend was that Plasmo-D efficiency was higher for infected image cells, average for uninfected image cells and the least for no cell photo.