NDLI: Automated Assessment of Erythrocyte Disorders Using Artificial Neural Network

Content Provider	IEEE Xplore Digital Library
Author	Zahir, S. Chowdhury, R. Payne, G.W.
Copyright Year	2006
Description	Author affiliation: Northern British Columbia Univ., BC (Zahir, S.; Chowdhury, R.; Payne, G.W.)
Abstract	In this paper, we employ artificial neural network (ANN) together with image analysis techniques to automate the assessment of erythrocyte disorders using blood parameters such as red blood cell (RBC) count, hemoglobin (Hgb) level, and mean corpuscular hemoglobin (MCH). The neural network is trained using 800 blood sample images collected from the Prince George-EC, Hospital. The images are captured using a high-resolution digital camera mounted on a microscope. The red, green, and blue values of each image are fed as the input of the neural network. The Hospital RBC, Hgb values of the samples measured using hydrodynamic focused analyzer (CELL-DYN 3200 System) are provided as the target values during training. Several variations of the back propagation-learning algorithm were applied for training. The trained network is tested against 200 blood samples. The output results are compared with those of Hospital laboratory and found to be near identical, most of which are within 5% margin of error, and are much significantly better than those published. The proposed method is simple, fast, accurate, and can be a crucial step in automating laboratory reporting
Starting Page	776
Ending Page	780
File Size	404974
Page Count	5
File Format	PDF
ISBN	0780397533
DOI	10.1109/ISSPIT.2006.270903
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2006-08-27
Publisher Place	Canada
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Artificial neural networks Hospitals Laboratories Image analysis Red blood cells Digital cameras Microscopy Neural networks Hydrodynamics Focusing blood analysis back propagation learning momentum artificial neural network
Content Type	Text
Resource Type	Article

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