Next Article in Journal
Analysis of Instability Mode and Limit Support Pressure of Shallow Tunnel Face in Sands
Previous Article in Journal
Experimental and Numerical Investigation on Bearing Capacity of Circumferential Joint of New Spatial Steel Tubular Grid Arch in Mined Tunnel
Article

An Intelligent Iris Based Chronic Kidney Identification System

1
Department of Electrical Engineering, Riphah International University, Islamabad 46000, Pakistan
2
Department of Electrical and Computer Engineering, Abbottabad Campus, COMSATS University Islamabad, Khyber Pakhtunkhwa, Abbottabad 22060, Pakistan
3
UCERD Pvt Ltd. Islamabad, Islamabad 44000, Pakistan
4
Department of Biomedical Engineering, Narowal Campus, University of Engineering and Technology Lahore, Punjab, Narowal 54890, Pakistan
5
Barcelona Supercomputing Center(BSC-CNS), E08034 Barcelona, Spain
*
Authors to whom correspondence should be addressed.
Symmetry 2020, 12(12), 2066; https://doi.org/10.3390/sym12122066
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
Show Figures

Figure 1

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. https://doi.org/10.3390/sym12122066

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. https://doi.org/10.3390/sym12122066

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. https://doi.org/10.3390/sym12122066

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop