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An Intelligent Iris Based Chronic Kidney Identification System

Department of Electrical Engineering, Riphah International University, Islamabad 46000, Pakistan
Department of Electrical and Computer Engineering, Abbottabad Campus, COMSATS University Islamabad, Khyber Pakhtunkhwa, Abbottabad 22060, Pakistan
UCERD Pvt Ltd. Islamabad, Islamabad 44000, Pakistan
Department of Biomedical Engineering, Narowal Campus, University of Engineering and Technology Lahore, Punjab, Narowal 54890, Pakistan
Barcelona Supercomputing Center(BSC-CNS), E08034 Barcelona, Spain
Authors to whom correspondence should be addressed.
Symmetry 2020, 12(12), 2066;
Received: 20 November 2020 / Revised: 5 December 2020 / Accepted: 8 December 2020 / Published: 12 December 2020
(This article belongs to the Section Computer and Engineering Science and Symmetry/Asymmetry)
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 using 2000 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. View Full-Text
Keywords: iridology; health-care; embedded computer vision; artificial intelligence iridology; health-care; embedded computer vision; artificial intelligence
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MDPI and ACS Style

Muzamil, S.; Hussain, T.; Haider, A.; Waraich, U.; Ashiq, U.; Ayguadé, E. An Intelligent Iris Based Chronic Kidney Identification System. Symmetry 2020, 12, 2066.

AMA Style

Muzamil S, Hussain T, Haider A, Waraich U, Ashiq U, Ayguadé E. An Intelligent Iris Based Chronic Kidney Identification System. Symmetry. 2020; 12(12):2066.

Chicago/Turabian Style

Muzamil, Sohail, Tassadaq Hussain, Amna Haider, Umber Waraich, Umair Ashiq, and Eduard Ayguadé. 2020. "An Intelligent Iris Based Chronic Kidney Identification System" Symmetry 12, no. 12: 2066.

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