  According to the World Health Organization(WHO), malaria is estimated to have
killed 627,000 people and infected over 241 million people in 2020 alone, a 12%
increase from 2019. Microscopic diagnosis of blood cells is the standard
testing procedure to diagnose malaria. However, this style of diagnosis is
expensive, time-consuming, and greatly subjective to human error, especially in
developing nations that lack well-trained personnel to perform high-quality
microscopy examinations. This paper proposes Mass-AI-Scope (MAIScope): a novel,
low-cost, portable device that can take microscopic images and automatically
detect malaria parasites with embedded AI. The device has two subsystems. The
first subsystem is an on-device multi-layered deep learning network, that
detects red blood cells (RBCs) from microscopic images, followed by a malaria
parasite classifier that recognizes malaria parasites in the individual RBCs.
The testing and validation demonstrated a high average accuracy of 89.9% for
classification and average precision of 61.5% for detection models using
TensorFlow Lite while addressing limited storage and computational capacity.
This system also has cloud synchronization, which sends images to the cloud
when connected to the Internet for analysis and model improvement purposes. The
second subsystem is the hardware which consists of components like Raspberry
Pi, a camera, a touch screen display, and an innovative low-cost bead
microscope. Evaluation of the bead microscope demonstrated similar image
quality with that of expensive light microscopes. The device is designed to be
portable and work in remote environments without the Internet or power. The
solution is extensible to other diseases requiring microscopy and can help
standardize automation of disease diagnosis in rural parts of developing
nations.
