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dc.contributor.authorMuzamil, Sohail
dc.contributor.authorHussain, Tassadaq
dc.contributor.authorHaider, Amna
dc.contributor.authorWaraich, Umber
dc.contributor.authorAshiq, Umair
dc.contributor.authorAyguadé Parra, Eduard
dc.contributor.otherUniversitat Politècnica de Catalunya. Departament d'Arquitectura de Computadors
dc.contributor.otherBarcelona Supercomputing Center
dc.date.accessioned2021-01-07T13:03:51Z
dc.date.available2021-01-07T13:03:51Z
dc.date.issued2020-12-12
dc.identifier.citationMuzamil, S. [et al.]. An intelligent iris based chronic kidney identification system. "Symmetry", 12 Desembre 2020, vol. 12, núm. 12, article 2066, p. 1-14.
dc.identifier.issn2073-8994
dc.identifier.urihttp://hdl.handle.net/2117/334989
dc.description.abstractIn 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.
dc.description.sponsorshipThis work is supported by the Higher Education Commission (HEC) of Pakistan under the Technology Development Fund with project Number TDF03-097 and National Research Program for Universities (NRPU) with project grant no. 8153/Federal/NRPU/R&D/HEC/2017.
dc.format.extent14 p.
dc.language.isoeng
dc.rightsAttribution 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectÀrees temàtiques de la UPC::Informàtica::Aplicacions de la informàtica::Bioinformàtica
dc.subjectÀrees temàtiques de la UPC::Informàtica::Intel·ligència artificial
dc.subject.lcshArtificial intelligence -- Medical applications
dc.subject.lcshKidneys -- Diseases -- Diagnosis
dc.subject.lcshIridology
dc.subject.otherHealth-care
dc.subject.otherEmbedded computer vision
dc.titleAn intelligent iris based chronic kidney identification system
dc.typeArticle
dc.subject.lemacIntel·ligència artificial -- Aplicacions a la medicina
dc.subject.lemacRonyons -- Malalties -- Diagnòstic
dc.subject.lemacIridologia
dc.contributor.groupUniversitat Politècnica de Catalunya. CAP - Grup de Computació d'Altes Prestacions
dc.identifier.doi10.3390/sym12122066
dc.description.peerreviewedPeer Reviewed
dc.relation.publisherversionhttps://www.mdpi.com/2073-8994/12/12/2066
dc.rights.accessOpen Access
local.identifier.drac30145011
dc.description.versionPostprint (published version)
local.citation.authorMuzamil, S.; Hussain, T.; Haider, A.; Waraich, U.; Ashiq, U.; Ayguadé, E.
local.citation.publicationNameSymmetry
local.citation.volume12
local.citation.number12, article 2066
local.citation.startingPage1
local.citation.endingPage14


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Except where otherwise noted, content on this work is licensed under a Creative Commons license : Attribution 4.0 Generic