  Malaria is a parasitic infection that poses a significant burden on global
health. It kills one child every 30 seconds and over one million people
annually. If diagnosed in a timely manner, however, most people can be
effectively treated with antimalarial therapy. Several deaths due to malaria
are byproducts of disparities in the social determinants of health; the current
gold standard for diagnosing malaria requires microscopes, reagents, and other
equipment that most patients of low socioeconomic brackets do not have access
to. In this paper, we propose a convolutional neural network (CNN) architecture
that allows for rapid automated diagnosis of malaria (achieving a high
classification accuracy of 98%), as well as a deep neural network (DNN) based
three-dimensional (3D) modeling algorithm that renders 3D models of parasitic
cells in augmented reality (AR). This creates an opportunity to optimize the
current workflow for malaria diagnosis and demonstrates potential for deep
learning models to improve telemedicine practices and patient health literacy
on a global scale.
