Use of artificial neural networks to accurately identify Cryptosporidium oocyst and Giardia cyst images

Applied and Environmental Microbiology

Volumn 71, Issue 1, 2005, Pages 80-84

  Widmer, Kenneth W ^a   Srikumar, Deepak ^a   Pillai, Suresh D ^a  

^a TEXAS A AND M UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ANTIBODIES; CAMERAS; DISEASES; FLUORESCENCE; IMAGE PROCESSING; IMMUNOLOGY; PATHOLOGY; PROTOZOA;

CRYPTOSPORIDIUM PARVUM; GIARDIA CYSTS; GIARDIA LAMBLIA; PATHOGENS;

NEURAL NETWORKS;

ARTIFICIAL NEURAL NETWORK; BACTERIUM; IDENTIFICATION METHOD;

ARTICLE; ARTIFICIAL NEURAL NETWORK; CAMERA; CONTROLLED STUDY; CRYPTOSPORIDIUM; CRYPTOSPORIDIUM PARVUM; GIARDIA; GIARDIA LAMBLIA; IMMUNOFLUORESCENCE TEST; MICROORGANISM DETECTION; NONHUMAN; OOCYST; VALIDATION PROCESS;

ANIMALS; CRYPTOSPORIDIUM PARVUM; FLUORESCENT ANTIBODY TECHNIQUE; GIARDIA LAMBLIA; IMAGE PROCESSING, COMPUTER-ASSISTED; NEURAL NETWORKS (COMPUTER); OOCYSTS; PARASITOLOGY; WATER;

CRYPTOSPORIDIUM; CRYPTOSPORIDIUM PARVUM; GIARDIA; GIARDIA INTESTINALIS; PROTOZOA;

EID: 12244258229     PISSN: 00992240     EISSN: None     Source Type: Journal    
DOI: 10.1128/AEM.71.1.80-84.2005     Document Type: Article

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