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 serious infectious disease that is responsible for over halfmillion deaths yearly worldwide. The major cause of these mortalities is lateor inaccurate diagnosis. Manual microscopy is currently considered as thedominant diagnostic method for malaria. However, it is time consuming and proneto human errors. The aim of this paper is to automate the diagnosis process andminimize the human intervention. We have developed the hardware and softwarefor a cost-efficient malaria diagnostic system. This paper describes themanufactured hardware and also proposes novel software to handle parasitedetection and life-stage identification. A motorized microscope is developed totake images from Giemsa-stained blood smears. A patch-based unsupervisedstatistical clustering algorithm is proposed which offers a novel method forclassification of different regions within blood images. The proposed methodprovides better robustness against different imaging settings. The core of theproposed algorithm is a model called Mixture of Independent Component Analysis.A manifold based optimization method is proposed that facilitates theapplication of the model for high dimensional data usually acquired in medicalmicroscopy. The method was tested on 600 blood slides with various imagingconditions. The speed of the method is higher than current supervised systemswhile its accuracy is comparable to or better than them.

Towards life cycle identification of malaria parasites using machine learning and Riemannian geometry