Title: Learning to predict diabetes from iris image analysis
Authors: U.M. Chaskar; M.S. Sutaone
Addresses: Department of Instrumentation and Control, College of Engineering, Wellesly Road, Shivajinagar, Pune Maharashtra 411 005, India ' Department of Electronics and Telecommunication, College of Engineering, Wellesly Road, Shivajinagar, Pune, Maharashtra 411 005, India
Abstract: Iris image analysis for clinical diagnosis is one of the most efficient non invasive diagnosis methods for determining organs' health status. Iridodiagnosis is an alternative branch of medical science which can be used for diagnostic purposes. To begin with we created database of eye images with clinical history of subject's emphasis on diabetic subject (Type II) in pathological laboratory/Hospital. The entire process involves various modules such as image quality assessment, segmentation of iris, iris normalisation and clinical feature classification for clinical diagnosis. The artificial neural network is used for training and classification purpose. The entire process shows classification accuracy of 72-75% between diabetic and non-diabetic subjects.
Keywords: noninvasive diagnosis; diabetics; iris image analysis; feature extraction; clinical predictions; diabetes prediction; iridodiagnosis; image quality; image segmentation; artificial neural networks; ANNs; eye images.
International Journal of Biomedical Engineering and Technology, 2012 Vol.9 No.1, pp.88 - 99
Published online: 12 Dec 2014 *Full-text access for editors Access for subscribers Purchase this article Comment on this article