Diabetic Detection Using Iris Images


For clinical diagnosis, Iris image analysis is one of the most efficient non-invasive diagnosis method which helps to determine the health status of organs. Though correct and timely diagnosis is critical, it is very essential requirement of medical science. From the literature survey that we have done, is observed that lot of modern technologies also fails in diagnose disease correctly. From different perspectives these attempts explore the area of diagnosis. Iridodiagnosis is the branch of medical science, with the help of which different diseases can be detected. Initially the images of eye are captured, database is created with their clinical history, features are found out and finally the classification is done whether the diabetic is present or not. Several classification methods can be used for training and classification purpose. We have implemented Machine learning KNN model, which will be useful in the diagnosis field which is faster and user friendly.


This approach is reliable and efficient in carrying out the function of recommending an iris diagnosis and authentication. This would improve the diagnostic phase and give it more trust. The iris is very useful for human authentication and recognition because of its unique and consistent spatial patterns. Such successful iris detection is used to recognize people and verify whether iris is impaired and distinguish the damaged portion of the human eye. Since every part of the iris reflects the various areas of the human body, it will be used to diagnose the different diseases without any more damage to the human body.

International Journal of Early Childhood Special Education (INT-JECSE)DOI:10.9756/INTJECSE/V14I5.30 ISSN: 1308-5581 Vol 14, Issue 05 2022

Authors: Mr.A.R.Aravind, Vidyasagar S, Karthick K.

Download full abstract:  https://iridology-research.com/pdf/20220712115245pm30.pdf