English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Tools annual

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

Malaria is a parasitic infection that poses a significant burden on globalhealth. It kills one child every 30 seconds and over one million peopleannually. If diagnosed in a timely manner, however, most people can beeffectively treated with antimalarial therapy. Several deaths due to malariaare byproducts of disparities in the social determinants of health; the currentgold standard for diagnosing malaria requires microscopes, reagents, and otherequipment that most patients of low socioeconomic brackets do not have accessto. In this paper, we propose a convolutional neural network (CNN) architecturethat allows for rapid automated diagnosis of malaria (achieving a highclassification accuracy of 98%), as well as a deep neural network (DNN) basedthree-dimensional (3D) modeling algorithm that renders 3D models of parasiticcells in augmented reality (AR). This creates an opportunity to optimize thecurrent workflow for malaria diagnosis and demonstrates potential for deeplearning models to improve telemedicine practices and patient health literacyon a global scale.

End-to-end Malaria Diagnosis and 3D Cell Rendering with Deep Learning