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Applied and Environmental Microbiology, January 2005, p. 80-84, Vol. 71, No. 1
0099-2240/05/$08.00+0     doi:10.1128/AEM.71.1.80-84.2005
Copyright © 2005, American Society for Microbiology. All Rights Reserved.

Use of Artificial Neural Networks To Accurately Identify Cryptosporidium Oocyst and Giardia Cyst Images

Kenneth W. Widmer, Deepak Srikumar, and Suresh D. Pillai*

Food Safety and Environmental Microbiology Program, Poultry Science Department, Institute of Food Science and Engineering, Texas A&M University, College Station, Texas

Received 19 May 2004/ Accepted 6 August 2004

Cryptosporidium parvum and Giardia lamblia are protozoa capable of causing gastrointestinal diseases. Currently, these organisms are identified using immunofluorescent antibody (IFA)-based microscopy, and identification requires trained individuals for final confirmation. Since artificial neural networks (ANN) can provide an automated means of identification, thereby reducing human errors related to misidentification, ANN were developed to identify Cryptosporidium oocyst and Giardia cyst images. Digitized images of C. parvum oocysts and G. lamblia cysts stained with various commercial IFA reagents were used as positive controls. The images were captured using a color digital camera at 400x (total magnification), processed, and converted into a binary numerical array. A variety of "negative" images were also captured and processed. The ANN were developed using these images and a rigorous training and testing protocol. The Cryptosporidium oocyst ANN were trained with 1,586 images, while Giardia cyst ANN were trained with 2,431 images. After training, the best-performing ANN were selected based on an initial testing performance against 100 images (50 positive and 50 negative images). The networks were validated against previously "unseen" images of 500 Cryptosporidium oocysts (250 positive, 250 negative) and 282 Giardia cysts (232 positive, 50 negative). The selected ANNs correctly identified 91.8 and 99.6% of the Cryptosporidium oocyst and Giardia cyst images, respectively. These results indicate that ANN technology can be an alternate to having trained personnel for detecting these pathogens and can be a boon to underdeveloped regions of the world where there is a chronic shortage of adequately skilled individuals to detect these pathogens.

* Corresponding author. Mailing address: 418D Kleberg Center, TAMUS 2472, Texas A&M University, College Station, TX 77843-2472. Phone: (979) 845-2994. Fax: (979) 845-1921. E-mail: spillai{at}poultry.tamu.edu.

Applied and Environmental Microbiology, January 2005, p. 80-84, Vol. 71, No. 1
0099-2240/05/$08.00+0     doi:10.1128/AEM.71.1.80-84.2005
Copyright © 2005, American Society for Microbiology. All Rights Reserved.





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