Multi-stage malaria parasite recognition by deep learning.

ABSTRACT:

Motivation

Malaria, a mosquito-borne infectious disease affecting humans and other animals, is widespread in tropical and subtropical regions. Microscopy is the most common method for diagnosing the malaria parasite from stained blood smear samples. However, this technique is time consuming and must be performed by a well-trained professional, yet it remains prone to errors. Distinguishing the multiple growth stages of parasites remains an especially challenging task.

Results

In this article, we develop a novel deep learning approach for the recognition of malaria parasites of various stages in blood smear images using a deep transfer graph convolutional network (DTGCN). To our knowledge, this is the first application of graph convolutional network (GCN) on multi-stage malar

SUBMITTER: Li S 

PROVIDER: S-EPMC8210472 | biostudies-literature | 2021 Jun

REPOSITORIES: biostudies-literature