A Novel Regression Based Model for Detecting Anemia Using Color Microscopic Blood Images
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Abstract
Modeling human blood components and disorders is a complicated task. Few researchers have attempted to automate the process of detecting anemia in human blood. These attempts have produced satisfactory but not highly accurate results. In this paper, we present an efficient method to estimate hemoglobin value in human blood and detect anemia using microscopic color image data. We have developed a logit regression model using one thousand (1000) blood samples that were collected from Prince George Hospital laboratory. The output results of our model are compared with the results of the same sample set using CELL-DYN 3200 System in Prince George Hospital laboratory, and found to be near identical. These results exceed those reported in the literature. Moreover, the proposed method can be im-plemented in hardware with minimal circuitry and nominal cost.
- J. B. Henry, “Clinical Diagnosis and Management by Laboratory Methods,” W. B. Saunders, Philadelphia, 2001.
- http://www.nlm.nih.gov/medlineplus
- M. Rendell, M. Anderson, W. Schlueter, J. Mailliard, D. Honigs and R. Rosenthal, “Determination of Hemoglobin Levels in The Finger Using Near Infrared Spectroscopy”, Journal of Clinical & Laboratory Haematology, Vol. 25, No. 2, April 2003, pp. 93-97.
- J. P. Greer, J. Foerster, J. N. Lukens, G. M. Rodgers, F. Paraskevas and B. Glader, “Wintrobe’s Clinical hematology,” 11th Edition, Lippincott Williams & Wilkins, Philadelphia, 2003.
- G. Ongun, U. Halici, K. Leblebicioglu and V. Atalay, “Feature Extraction and Classification of Blood Cells for an Automated Differential Blood Count System,” International Joint INNS-IEEE Conference on Neural Networks, Washington DC, Vol. 4, 2001, pp. 2461-2466.
- CELL-DYN 3200 System Training Guide, 2004.
- S. Zahir, C. G. Rejaul and W. Payne, “Automated Assessment of Erythrocyte Disorders Using Artificial Neural Network,” IEEE International Symposium on Signal Processing and Information Technology, Vancouver, 2006, pp. 776-780.
- H. Ranganath and N. Gunasekaran, “Artificial Neural Network Approach in Estimation of Hemoglobin in Human Blood,” International Computer Engineering Conference on New Technologies for the Information Society, ICENCO, Cairo, 2004, pp. 341-344.