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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| Yayımlandı: | Appl Environ Microbiol |
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| Asıl Yazarlar: | , , |
| Materyal Türü: | Artigo |
| Dil: | Inglês |
| Baskı/Yayın Bilgisi: |
American Society for Microbiology (ASM)
2002
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| Konular: | |
| Online Erişim: | 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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