An Efficient Machine Learning Approach to Nephrology through Iris Recognition



Iridology is a technique in science used to analyze color, patterns, and various other properties of the iris to assess an individual’s general health. Few regions in the iris are connected by nerves coming from different organs of body, this shows some special unique qualities which is advantageous along with which assist in psychological condition, particular organ conditions and construction of the body. The structural and designed patterns present on specific part of iris represent the level of intensity of disorder caused by the organs. This method of approach can be employed as reasonable and logical guidelines for the detection and identification of disorders. Therefore, after scanning the image of iris advance study of disorder can be carried out for detecting the condition of organ. Initially by the service of an adaptive histogram, the image of eye should be separated from part of the image captured. Next the images of iris are classified and recognized using machine learning algorithm Support Vector machine or Support Vector Networks. The features are extracted from images of iris using white Gaussian filters which are then used as a feature descriptor. These descriptors count the occurrences of gradient orientation and magnitude in localized portions of an image. Then convert the image of iris to a gray scaled image, final image is standardized. Next is to convert it into rectangular shape and then assembling the HMM images of eyes related to the kidney. The final level is to diagnose the edge of image of iris HMM. By analysing end results, condition of the organ can be diagnosed and results can be obtained from the iris recognition system.


In this system, kidney problems are being detected by analysing the patterns of the iris in the eyes. Each and every nerve that connected to the inner organ are also has a connection to the eyes. Therefore, it is possible to understand the inner organ condition only by analysing the human eyes. Here, the nerve that is connected to the eyes and kidney is considered for the detection of kidney problems. The lower left side nerve for the left eye and the lower right side nerve for the right eye is considered to test the condition of the kidney. The HMM (Hidden Markov Model) is the main algorithm that helps to detect kidney problems. The future work is to implement this model to detect the other inner organ problems. Such as Condition of Stomach, heart state, Liver condition, Lung’s condition, Condition of the throat, thyroid problem, Small intestine, descend. An Efficient Approach to Nephrology through IRIS Recognition will implement using Machine Learning and MATLAB. It is used to detect the various kidney problem by checking the iris image. Hence it is used as a real-world criterion for scanning kidney diseases based on iris images. Further study about the condition of the organ is done by checking out the iris images. The program is able to perform the process of classification of five samples of 100 data like Diabetic kidney recognition, stone kidney recognition, kidney failure, kidney chronic failure and kidney normal state.

An Efficient Machine Learning Approach to Nephrology through Iris Recognition *Divya C D, *Gururaj H L, *Rohan R, *Bhagyalakshmi V, *Rashmi H A, *Domnick A, +Francesco Flammini * *Vidyavardhaka College of Engineering, Department of Computer Science and Engineering, Gokulam 3rd stage vijaynagar, Mysuru 570002, India. + University of Applied Sciences and Arts of Southern Switzerland

Full Abstract: