Detection and stage classification of Plasmodium falciparum from images of Giemsa stained thin blood films using random forest classifiers.

ABSTRACT:

Background

The conventional method for the diagnosis of malaria parasites is the microscopic examination of stained blood films, which is time consuming and requires expertise. We introduce computer-based image segmentation and life stage classification with a random forest classifier. Segmentation and stage classification are performed on a large dataset of malaria parasites with ground truth labels provided by experts.

Methods

We made use of Giemsa stained images obtained from the blood of 16 patients infected with Plasmodium falciparum. Experts labeled the parasite types from each of the images. We applied a two-step approach: image segmentation followed by life stage classification. In segmentation, we classified each pixel as a parasite or non-parasite pixel using a ran

SUBMITTER: Abbas SS 

PROVIDER: S-EPMC7585298 | biostudies-literature | 2020 Oct

REPOSITORIES: biostudies-literature