Automated detection and staging of malaria parasites from cytological smears using convolutional neural networks.

ABSTRACT: Microscopic examination of blood smears remains the gold standard for laboratory inspection and diagnosis of malaria. Smear inspection is, however, time-consuming and dependent on trained microscopists with results varying in accuracy. We sought to develop an automated image analysis method to improve accuracy and standardization of smear inspection that retains capacity for expert confirmation and image archiving. Here, we present a machine learning method that achieves red blood cell (RBC) detection, differentiation between infected/uninfected cells, and parasite life stage categorization from unprocessed, heterogeneous smear images. Based on a pretrained Faster Region-Based Convolutional Neural Networks (R-CNN) model for RBC detection, our model performs accurately, with an average prec

SUBMITTER: Davidson MS 

PROVIDER: S-EPMC8724263 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature