Abstract:
In recent years, the demand for alternative medical diagnostics of the human kidney or renal is growing, and some of the reasons behind this relate to its non-invasive, early, real-time,and pain-free mechanism. The chronic kidney problem is one of the major kidney problems,which require an early-stage diagnosis. Therefore, in this work, we have proposed and developed an Intelligent Iris-based Chronic Kidney Identification System (ICKIS). The ICKIS takes an image of human iris as input and on the basis of iridology a deep neural network model on a GPU-based supercomputing machine is applied. The deep neural network models are trained while using2000 subjects that have healthy and chronic kidney problems. While testing the proposed ICKIS on 2000 separate subjects (1000 healthy and 1000 chronic kidney problems), the system achieves iris-based chronic kidney assessment with an accuracy of 96.8%. In the future, we will work to improve our AI algorithm and try data-set cleaning, so that accuracy can be increased by more efficiently learning the features.
Conclusions and Future Work
In this work, an alternative medical diagnostic method, called the Intelligent Iris-based Chronic Kidney Identification System (ICKIS), is proposed. ICKIS detects chronic kidney disorders at an early stage while using an artificial intelligence algorithm and iridology. The proposed ICKIS uses humaniris as input and a deep neural network-based algorithm, in order to determine whether or not the subject has a chronic kidney problem. ICKIS has tested over 2000 subjects and the findings conclude that the device categorized subjects as healthy kidney and chronic kidney with an accuracy of 96.8%. The ICKIS algorithm is trained and tested by a GPU based distributed computing machine.In the future, we shall continue to detect more renal problems with our AI algorithm. We aim to find a method for assessing the problem of the chronic kidney for both kidneys independently.This provides a pre-diagnosis method and a better treatment of early stage kidney disease. We are now working to build and make our iris data-sets accessible to the research community for advanced health-care applications.
Author Contributions:
Sohail Muzamil1,2,*, Tassadaq Hussain1,3,*, Amna Haider3, Umber Waraich3,4,Umair Ashiq2and Eduard Ayguadé 51
Department of Electrical Engineering, Riphah International University, Islamabad 46000, Pakistan2
Department of Electrical and Computer Engineering, Abbottabad Campus, COMSATS University Islamabad, Khyber Pakhtunkhwa, Abbottabad 22060, Pakistan; umairashiq@cuiatd.edu.pk
UCERD Pvt Ltd. Islamabad, Islamabad 44000, Pakistan; amna@ucerd.com (A.H.);umberwarraich@uet.edu.pk (U.W.)4Department of Biomedical Engineering, Narowal Campus, University of Engineering and Technology Lahore, Punjab, Narowal 54890, Pakistan5Barcelona
Supercomputing Center (BSC-CNS), E08034 Barcelona, Spain; Eduard.ayguade@bsc.es*Correspondence: sohail_muzamil@cuiatd.edu.pk (S.M.); tassadaq@ucerd.com (T.H.)Received: 20 November 2020; Accepted: 8 December 2020; Published: 12 December 202
