Deep learning for microscopic examination of protozoan parasites

  • Chi Zhang
  • , Hao Jiang
  • , Hanlin Jiang
  • , Hui Xi
  • , Baodong Chen
  • , Yubing Liu
  • , Mario Juhas
  • , Junyi Li*
  • , Yang Zhang
  • *Corresponding author for this work

Research output: Contribution to journalReview articlepeer-review

Abstract

The infectious and parasitic diseases represent a major threat to public health and are among the main causes of morbidity and mortality. The complex and divergent life cycles of parasites present major difficulties associated with the diagnosis of these organisms by microscopic examination. Deep learning has shown extraordinary performance in biomedical image analysis including various parasites diagnosis in the past few years. Here we summarize advances of deep learning in the field of protozoan parasites microscopic examination, focusing on publicly available microscopic image datasets of protozoan parasites. In the end, we summarize the challenges and future trends, which deep learning faces in protozoan parasite diagnosis.

Original languageEnglish
Pages (from-to)1036-1043
Number of pages8
JournalComputational and Structural Biotechnology Journal
Volume20
DOIs
StatePublished - Jan 2022
Externally publishedYes

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 3 - Good Health and Well-being
    SDG 3 Good Health and Well-being

Keywords

  • Babesia
  • Deep learning
  • Evaluation metrics
  • Leishmania
  • Malaria
  • Microscopy
  • Plasmodium
  • Protozoan Parasite Dataset
  • Toxoplasma
  • Trichomonad
  • Trypanosome

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