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General Information
Dr. Lin Huang
Metropolitan State University of Denver, USA
It's my honor to take on the position of editor in chief of IJMLC. We encourage authors to submit papers concerning any branch of machine learning and computing.
IJMLC 2013 Vol.3(4): 376-379 ISSN: 2010-3700
DOI: 10.7763/IJMLC.2013.V3.342

Classification of Blood Types by Microscope Color Images

S. M. Nazia Fathima
Abstract—Blood typing is a method to tell what specific type of blood a person has. It is a mandatory that everyone should know their blood type. It is extremely useful in blood transfusions, donation, accidents and other emergencies. The blood type testing is typically made in laboratories by technicians. Such a procedure presents undesirable drawbacks: slowness and it presents non standardized accuracy since it depends on the operator's capabilities and tiredness. This paper presents a methodology to achieve a semi automated system for classification of blood types by microscope color images. This paper concerns with the ABO and Rh blood typing systems. The classification of blood types in microscopy images allows identifying the blood groups and Rh factor accurately. The proposed system first performs image pre-processing by histogram equalization and color correction and then a color space conversion from RGB to HSI is done. Then it extracts the color and texture features of the images using cumulative histogram and Haralick method respectively. Finally it classifies the blood type by support vector machine (SVM).

Index Terms—Blood group, color correction, color space conversion, cumulative histogram, Haralick, SVM.

S. M. Nazia Fathima is with the Department of Computer Science and Engineering, Sethu Institute of Technology, Virudhunagar, Tamilnadu, India (e-mail: smaizan@gmail.com).


Cite:S. M. Nazia Fathima, "Classification of Blood Types by Microscope Color Images," International Journal of Machine Learning and Computing vol.3, no. 4, pp. 376-379, 2013.

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