Identification of Cryptosporidium parvum Oocysts by an Artificial Neural Network Approach

Microscopic detection of Cryptosporidium parvum oocysts is time-consuming, requires trained analysts, and is frequently subject to significant human errors. Artificial neural networks (ANN) were developed to help identify immunofluorescently labeled C. parvum oocysts. A total of 525 digitized images...

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Detalhes bibliográficos

Publicado no:	Appl Environ Microbiol
Principais autores:	Widmer, Kenneth W., Oshima, Kevin H., Pillai, Suresh D.
Formato:	Artigo
Idioma:	Inglês
Publicado em:	American Society for Microbiology (ASM) 2002
Assuntos:	Public Health Microbiology
Acesso em linha:	https://ncbi.nlm.nih.govhttps://pmc.ncbi.nlm.nih.gov/articles/PMC123730/ https://ncbi.nlm.nih.govhttps://pubmed.ncbi.nlm.nih.gov/11872458/ https://ncbi.nlm.nih.govhttps://doi.org/10.1128/AEM.68.3.1115-1121.2002
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