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Deep learning for microscopic examination of protozoan parasites

期刊

出版社

ELSEVIER
DOI: 10.1016/j.csbj.2022.02.005

关键词

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

资金

  1. Shenzhen Science and Technol-ogy Innovation Commission (Shenzhen Basic Research Project) [JCYJ20180306172131515]
  2. Fundamental Research Funds for the Central Universities [HIT.NSRIF.2020064]
  3. Shenzhen Science and Technology Program [20200821222112001]

向作者/读者索取更多资源

Infectious and parasitic diseases pose a major threat to public health, and deep learning has shown remarkable performance in the diagnosis of protozoan parasites through microscopic examination. However, challenges and future trends still exist for deep learning in protozoan parasite diagnosis.
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. (C)& nbsp;2022 The Author(s). Published by Elsevier B.V. on behalf of Research Network of Computational and Structural Biotechnology.& nbsp;

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