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Editor-in-chief
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 2015 Vol. 5(3): 225-229 ISSN: 2010-3700
DOI: 10.7763/IJMLC.2015.V5.511

An Automated Tool for Non-contact, Real Time Early Detection of Diabetes by Computer Vision

Jamal Firmat Banzi and Zhaojun Xue
Abstract—There has been considerable progress in computer vision, artificial neural network and pattern recognition in the last two decades, and there has also much progress in medical imaging technology in recent years. Although images in digital form can be processed by basic image processing techniques, effective use of computer vision can provide much useful information for diagnosis and treatment. In this paper we integrate computer vision and iridology practice for the detection of diabetes. Using iridology iris image is evaluated by detecting the presence of broken tissues and change in color pattern. According to iridology the abnormality in an iris of the human eye represent the abnormality of the corresponding organ conferred by the iris chart. In this research we examine pancreas organ which is at position 01:45 – 02:15 for the right eye and 07:15-7:45 for the left eye according to Dr. Jensen iris chart. We applied two methods to reach our conclusion, visual inspection method and color coding method. The artificial neural network is used for training and classification purpose. The entire process is showing a high accuracy detection of abnormality of pancreas organ which led to diabetes. The final result is compared with the insulin normality test for verification.

Index Terms—Computer vision, diabetic, feature extraction, iris, iridodiagnosis.

The authors are with Tianjin University, Tianjin, China (e-mail: jamalbanzi@yahoo.com).

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Cite: Jamal Firmat Banzi and Zhaojun Xue, "An Automated Tool for Non-contact, Real Time Early Detection of Diabetes by Computer Vision," International Journal of Machine Learning and Computing vol. 5, no. 3, pp. 225-229, 2015.

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