An intelligent iris based chronic kidney identification system

symme y S S A icle An In elligen I is Based Ch onic Kidney Iden i ica ion Sys em Sohail Muzamil 1,2,* , Tassadaq Hussain 1,3,* , Amna Haide 3, Umbe Wa aich 3,4, Umai Ashiq 2and Edua d Ayguadé 5 1Depa men o Elec ical Enginee ing, Riphah In e na ional Uni e si y, Islamabad 46000, Pakis an 2Depa men o Elec ical and Compu e Enginee ing, Abbo abad Campus, COMSATS Uni e si y Islamabad, Khybe Pakh unkhwa, Abbo abad 22060, Pakis an; [email p o ec ed] 3UCERD P L d. Islamabad, Islamabad 44000, Pakis an; [email p o ec ed] (A.H.); [email p o ec ed] (U.W.) 4Depa men o Biomedical Enginee ing, Na owal Campus, Uni e si y o Enginee ing and Technology Laho e, Punjab, Na owal 54890, Pakis an 5Ba celona Supe compu ing Cen e (BSC-CNS), E08034 Ba celona, Spain; Edua [email p o ec ed] *Co espondence: [email p o ec ed] (S.M.); assadaq@uce d.com (T.H.) Recei ed: 20 No embe 2020; Accep ed: 8 Decembe 2020; Published: 12 Decembe 2020   Abs ac : In ecen yea s, he demand o al e na i e medical diagnos ics o he human kidney o enal is g owing, and some o he easons behind his ela e o i s non-in asi e, ea ly, eal- ime, and pain- ee mechanism. The ch onic kidney p oblem is one o he majo kidney p oblems, which equi e an ea ly-s age diagnosis. The e o e, in his wo k, we ha e p oposed and de eloped an In elligen I is-based Ch onic Kidney Iden i ica ion Sys em (ICKIS). The ICKIS akes an image o human i is as inpu and on he basis o i idology a deep neu al ne wo k model on a GPU-based supe compu ing machine is applied. The deep neu al ne wo k models a e ained while using 2000 subjec s ha ha e heal hy and ch onic kidney p oblems. While es ing he p oposed ICKIS on 2000 sepa a e subjec s (1000 heal hy and 1000 ch onic kidney p oblems), he sys em achie es i is-based ch onic kidney assessmen wi h an accu acy o 96.8%. In he u u e, we will wo k o imp o e ou AI algo i hm and y da a-se cleaning, so ha accu acy can be inc eased by mo e e icien ly lea ning he ea u es. Keywo ds: i idology; heal h-ca e; embedded compu e ision; a i icial in elligence 1. In oduc ion The p ime unc ion o he human u ina y sys em [ 1 ] is o elimina e was e ma e ials and p oduc s om he body. In o de o main ain homeos asis, he kidney plays an impo an ole in he u ina y sys em. The was e ma e ials; mine al [ 2 ], i amin [ 3 ], ni ogenous was e [ 4 ], ammonia [ 5 ], and c ea inine [ 6 ] a e con e ed in o u ine, which is aken ou o he body h ough u e e s, bladde , and u e h a [ 1 ]. Some o he unc ions o he u ina y sys em include: he egula ion o plasma ionic composi ion [ 7 ], plasma olume [ 8 ], sec e ion o ho mones, plasma hyd ogen ion concen a ion (pH), and plasma osmola i y [9]. The kidney diso de leads o: (a) diabe es, (b) hype ension, (c) he edi a y (inhe i ance), (d) u ological, (e) acu e enal ailu e, ( ) in ec ion o he u ina y ac , kidney (g) glome ula disease, and (h) ascula enal [ 10 – 13 ]. A o esaid p oblems inc ease he isk o majo a al p oblems, such as: ca dio ascula issues [14], human immunode iciency i us “HIV” [15], and mala ia [16]. The dea h a e due o kidney ailu e om 2005 o 2013 is inc eased by 32% [ 17 ]. In 2010, 2.3 o 7.1 million people los hei li es because o non-accessibili y o kidney dialysis [ 18 ]. App oxima ely Symme y 2020,12, 2066; doi:10.3390/sym12122066 www.mdpi.com/jou nal/symme y Symme y 2020,12, 2066 2 o 14 1.7 million people died annually only due o acu e kidney inju ies [ 19 ]. The Wo ld Heal h O ganiza ion (WHO) epo published in 2018 e ealed ha o e all 5–10 million people los hei li es due o u ina y diso de s [20]. These s a is ics show ha kidney ela ed diseases a e inc easing day-by-day. Ch onic kidney disease is one o he majo p oblems o he u ina y sys em. O he kidney- ela ed p oblems a e: (1) Au osomal Dominan Polycys ic Kidney Disease (ADPKD) [ 21 ], which is a gene ic diso de ha is caused by he g ow h o nume ous cys s in he kidneys; (2) he long e m in lamma ion and sca ing o he glome uli known as ch onic glome uloneph i is (CGN) [ 22 ]; (3) ch onic in e s i ial neph i is (CIN) [ 23 ], in which swelling occu s in be ween he kidney ubules; and, (4) eno ascula disease (RVD) [24], which is ela ed o he a e ies o he kidneys. Figu e 1desc ibes he pe cen age o ch onic kidney p oblems in unde de eloped coun ies (like Pakis an), whe e mos o he popula ion is a ec ed by Ch onic Glome uloneph i is (CGN). The main cause o CGN is ei he he gene ic ans e o he p oblem o he immune sys em p oblem. The body mis akenly a acks i s own cells due o he immune sys em p oblem [ 25 ]. Diabe ic neph opa hy is ano he se ious kidney p oblem ha is caused by high blood glucose le els [ 26 ]. Diabe ic neph opa hy is nex o CGN, a ec ing 23% o he popula ion. Ch onic In e s i ial Neph i is (CIN) is also one o he majo kidney p oblem wi h 17% a ec ees, his p oblem has many di e en e iologies, which a e ca ego ized based on p oblem-speci ic indings. In con en ional medical p ocedu es, pa hological ma ke s, abno mali ies in he blood o u ine, imaging es s, and Glome ula Fil e Ra e (GFR) may e eal he kidney p oblems. Acco ding o he Kidney Disease Ou comes Quali y Ini ia i e (K/DOQI) o he Na ional Kidney Founda ion (NKF), he easons o ch onic kidney disease could be: (a) kidney damage o (b) a dec ease in GFR o 60 mL/min./1.73 m 2 o less, o h ee o mo e han h ee-mon hs [ 27 ]. The bes a ailable me hod o es ima ing GFR is o de e mine he le el o c ea inine in blood and u ine [ 28 ]. The majo causes o ch onic kidney ailu e a e he delayed diagnosis, inancial p oblems o he pa ien , and he una ailabili y o expe doc o s in emo e a eas [ 29 , 30 ]. The ea lie de ec ion o ch onic kidney disease can educe he mo bidi y [31] and mo ali y a e [32,33]. Al e na i e medicine [ 34 ] and ea ly diagnoses [ 35 ] a e he need and demand o u u e heal hca e echnology. This is because i p o ides he non-in asi e, and eal- ime diagnoses o human dys unc ional o gans o imp o e heal h quali y. The usage o an al e na i e p e-diagnosis me hod [ 36 ] can sa e complica ions, cos , and delay diagnosis. I idology [ 37 ] o i is s udy is an al e na i e diagnosing me hod ha is used o p e-diagnose human dys unc ional o gans. I idology ca ego izes he human i is acco ding o each o gan o he body [ 38 ]. The weakness o damage o an o gan is e lec ed by he ma ks, spo s, o pa e ns in he co esponding i is egion [ 39 ]. These i is-pa e ns indica e he p oblems o he speci ic body o gans [ 40 ]. Fo ins ance, i he e is a p oblem wi h he s omach, hen i shows changes in he i is a ound he pupil. Simila ly, changes in kidneys appea a he bo om edge o he i is. The igh i is indica es he o gans on he igh side o he human body, while he le side o he i is e lec s he o gans on he le side o he body. Figu e 2shows di e en egions ha a e ela ed o almos all human body o gans o unc ions [41]. In his p oposed esea ch wo k, we de elop an I is-based Ch onic Kidney Iden i ica ion Sys em (ICKIS) o an ea ly assessmen o kidney p oblems. The ICKIS akes i is images as inpu and pe o ms gene al p e-diagnosis o ch onic kidney o gan diso de ha is based on an i idology cha and a i icial in elligen algo i hm. The algo i hm is execu ed on a GPU-based supe compu ing machine. The ICKIS de e mines he se e i y o damage o he human kidney by iden i ying he ex u e and colo o ma k o lesion (in he i is kidney egion) while using a CNN based a i icial in elligence algo i hm. The ICKIS is execu ed on GPU based supe compu ing sys em. While es ing ICKIS on 2000 (heal hy and kidney p oblems) subjec s, he esul s con i m ha he ICKIS iden i ied he ch onic kidney p oblem wi h an accu acy o 96.8%. The p oposed ICKIS sys em would help clinicians o pe o m quick and ea ly p e-diagnoses o he human ch onic kidney ha would no only sa e ime, bu also dec ease he inancial bu den o e he pa ien s and he na ional heal h sys em. The p oposed echnique will open a new pa adigm in he ield o medicine by p o iding cos -e ec i e, quick, and painless p e-diagnosis. Symme y 2020,12, 2066 3 o 14 Figu e 1. Pie cha showing pe cen age ch onic kidney diseases in Pakis an [42]. Figu e 2. I idology Cha : Showing Kidney Region o In e es in Le and Righ I ides. 2. Rela ed Wo k Va ious esea ch has been conduc ed on he human i is o iden i y human heal h dys unc ionali ies and i idology e ec i eness. SE. Hussein e al. [ 39 ] use i idology o diagnose kidney p oblems and ocus on Ch onic Renal Failu e (CRF). The mul iplex Handheld came a is used o ge i is images o 340 subjec s, among hem 168 do no ha e CRF while 172 ha ing CRF ( es ed by c ea inine es ). Di e en echniques a e used o segmen a ion and no maliza ion, such as (a) Ci cula Hough T ans o m (CHT) [ 43 ], (b) Homogeneous Rubbe Shee Model (RSM), and (c) Gabo Fil e . Fea u es a e being ex ac ed while using 2D-wa ele T ans o m and, o classi ica ion, an Adap i e Neu o-Fuzzy In e ence Sys em is used. The CRF is ound 82% and 93% co ec ly o each subjec ha ing kidney p oblems o no , espec i ely. This p oposed esea ch gi es p omising esul s o he p e-diagnosis o kidney CRF. To diagnose medical condi ions, Sa a Ame i a e al. [ 44 ] ha e pe o med esea ch wo k while using human i ises. The CASIA i is image da abase has been used du ing he esea ch wo k. In o de o de ec ci cula shapes, CHT is used, while Hessian analysis is used o geome ical enhancemen [ 45 ]. Mul iscale blobness [ 46 ] is used o spo ou di e en sizes in he i is egion o he kidney egion and connec ed componen labeling is done in o de o g oup pixels o he candida e egion. Thei esul s con i m ha he algo i hm iden i ied a subjec wi h CRF and no-CRF wi h an accu acy o 82% and 93%, espec i ely. Symme y 2020,12, 2066 4 o 14 Agus P ayi no e al. [ 47 ] unde ake hei s udies on kidney complica ions ha a e caused by diabe es. The i is egion o in e es (ROI) is used acco ding o he i idology cha . To analyze he ROI, colo cons ancy [ 48 ] and independen componen analysis [ 49 ] a e used. They use Dino-Li e Ve . 2.0 o cap u ing i is images. The esul s show ha 76% o pa ien s a e diabe ic wi h kidney complica ions. The esul s o he p oposed sys em a e e i ied by he pa ien medical c ea inine le el. Adhi e al. [ 50 ] classi y he las s age o kidney CRF using he i idology cha . They cap u e i is images o 82 subjec s using he Dino-Li e Digi al i is scope AMH-RUT came a sys em ha ing 1.3 M pixels esolu ion and 30 ame a e. The wa e shed me hod o ea u e segmen a ion is used in o de o examine he b oken issues in he i is. The au ho uses he mean o he g adien , mean o he wa e shed, mean o wa e shed bina y, and size o b oken issue as ea u es, which a e inpu ed o Suppo Vec o Machine (SVM) o machine lea ning-based classi ica ion. Du ing he esea ch, 61 hemodialyses and 21 heal hy pa ien s a e used o analysis and he esul s show ha he algo i hm iden i ied hemodialysis pa ien s wi h an accu acy o 87.5%. Saman and Aga wal [ 51 ] use di e en machine lea ning echniques, including (a) Bina y T ee model, (b) SVM, (c) Adap i e Boos ing model, (d) Neu al Ne wo k, and (e) Random Fo es model o classi ica ion and diagnosis o diabe es. A o esaid echniques use I is-SCAN-2 wi h c oss-ma ch echnology o ob ain g ay in a ed images o he in es iga ion o (a) s a is ical, (b) ex u e, and (c) disc e e wa ele -based ea u es. The au ho s pe o m modi ied CHT o ex ac he a o emen ioned ea u es. Rubbe -shee no maliza ion is used o map i is ea u es on ixed ec angula ep esen a ion. They di ided 338 pa ien s in o wo g oups, one ha ing a sign o diabe es include 180 subjec s, and he o he wi h no sign ha e 158. The esul s con i m ha he model iden i ied diabe es wi h an accu acy o 89.63%. Rossi E win e al., in [ 52 ], use i idology o de elop a da abase o colon diso de . Au onomic Ne ous W ea h (ANW) o he i is is ocused on diges i e p oblems. Hough ans o m and RSM a e used o ea u e ex ac ion. Six y subjec s a e used, in which 25 a e con olled while 35 a e p o en colon diso de . The esul s show an 8% e o . This esea ch shows p og essi e esul s and concludes ha , o ob ain be e and eliable esul s, a la ge da a-se is equi ed. Jamal e al. [ 53 ] wo k o de elop a ool o de ec ing diabe es h ough he co esponding egion o he panc eas o gan, while using colo coding and isual inspec ion. Segmen a ion is done by Daugman’s app oach and no maliza ion is done while using RSM; adap i e his og am equaliza ion o he spa ial domain me hod, which di ec ly ope a es on pixels, is used o image enhancemen . Fea u es o 20 × 20 ROI a e ex ac ed while using P incipal Componen Analysis (PCA). Feed o wa d-back p opaga ion based, A i icial Neu al Ne wo k is applied o classi ica ion wi h he Le engu g–Ma qua d back-p opaga ion o weigh and bias o ne wo k as aining unc ion. Ou o 10 subjec s, eigh subjec s a e iden i ied as diabe ic subjec s. La e on, all o hese eigh a e e i ied as diabe ic ype II using an insulin no mali y es . 100 % accu acy is achie ed o he panc eas o gan. UCERD P i a e Limi ed [ 54 ], I idology Resea ch G oup [ 55 ], has de eloped imaging sys em a chi ec u e [ 56 ] and algo i hms [ 57 ] o i idology based dys unc ional o gans iden i ica ion. The g oup p oposed in elligen algo i hms o heal h-ca e. I uses high-pe o mance a chi ec u e [ 58 , 59 ] ha manages an eno mous olume o pa ien images [ 60 , 61 ] in o ma ion, and e ec i ely uses hem o make a decision. 3. Me hodology: An In elligen I is Based Ch onic Kidney Iden i ica ion Sys em This sec ion explains he me hodology o he I is-based Ch onic Kidney Iden i ica ion Sys em (ICKIS). The sec ion is subdi ided in o ou main sec ions: he Kidney Pa hology, he I is-Acquisi ion Sys em, he P ocessing Sys em, and he A i icial In elligence (AI) Algo i hm. 3.1. Kidney Pa hology The sec ion explains he pa hology o he kidney agains ch onic kidney disease while using con en ional me hods and i idology echniques. Symme y 2020,12, 2066 5 o 14 3.1.1. Con en ional Me hod Con en ional me hods use Glome ula Fil a ion Ra e (GFR), in o de o de e mine he s age o ch onic kidney disease. In GFR, samples o he pa ien ’s blood and u ine a e compa ed. A es o c ea inine using he blood sample is conduc ed in o de o measu e GFR (i c ea inine alue inc eases, he GFR dec eases). GFR measu es millili e s o was e ha is il e ed by kidneys in a minu e. The kidneys o a heal hy pe son il e o e 90 mL o was e pe minu e [ 27 ]. The ollowing a e he s ages o ch onic kidney disease conce ning he GFR desc ibed by he Kidney Disease Ou come Quali y Ini ia i e (K/DOQI) [62]: S age1: (GFR > 90 mL/min./1.73 m 2 ) kidney unc ioning is no mal up o 90%, bu wi h mino damage (p o ein in u ine). S age 2: (GFR 60–89 mL/min./1.73 m 2 ) Acu e Kidney Disease, mild loss in kidney unc ioning, and i is wo king up o 60 o 89%. S age 3: (GFR 30–59 mL/min./1.73 m 2 ) Ch onic Kidney Disease, mode a e loss o kidney unc ioning, and i is dec eased o 45 o 59%, bu i may lead o a mode a e o se e e loss o kidney unc ioning, up o 30 o 44%. S age 4: (GFR 15–29 mL/min./1.73 m 2 ) Se e e Ch onic Disease, se e e loss o kidney unc ioning om 15 o 29%. S age 5: (GFR < 15 mL/min./1.73 m 2 ) End-S age Renal Disease (ESRD) i.e., kidney ailu e s age. A his s age, he e is a o al loss o less han 15% unc ioning. The GFR le el keeps on changes wi h ime o di e en age g oups. In hese ea ly s ages, GFR alone does no secu e he inding ha ’s why o he kidney inspec ion es s like (a) blood, (b) u ine, and (c) kidney scan can be used in o de o de e mine he kidney anomalies. In s age 4 and s age 5, he se e e symp oms a e being el by he pa ien s, which leads him/he o ch onic kidney ailu e. In his esea ch wo k, pa ien s who a e g ouped unde s ages 4 and 5 a e ocused, as indica ed by hei GFR, and he emphasis is on he p esence o absence o ch onic kidney disease o s age 4 and s age 5. 3.1.2. I idology In he ligh o clinical assessmen s om pa ien da a and epo s, a ious pa e ns and ea u es o ch onic kidney a e illus a ed by u ilizing he i idology cha (shown in Figu e 2). The i idology cha ha is shown in Figu e 2is di ided in o 12 o (360 ◦ ) po ions. The a ge ed po ion o ch onic kidney disease (CKD) agains s age 4 and s age 5 lies unde posi ion 5.35–5.95 (252 ◦ –268 ◦ ) o he igh eye and in posi ions 6.05–6.60 (272 ◦ –288 ◦ ) o he le eye. The igh eye ep esen s he igh enal o gan and he le eye ep esen s he le enal o gan. Figu e 3shows he egion o in e es (ROI) and s age 4 and 5, ma ks and pa e ns o bo h he le and igh eyes. (a) (b) Figu e 3. I idology egion o in e es (ROI) Ma ks and pa e ns showing kidney p oblems ( a ) Righ Eyes (b) Le Eyes. Symme y 2020,12, 2066 6 o 14 I is images o subjec s ha ing ch onic kidney p oblem con ained he ollowing pa e ns and ma ks: (a) A long solid black line. (b) One o mo e, la ge o small open lesion. (c) Change in colo o he i is issue. As kidney p oblems ge wo se, he GFR le el dec eases, and, simila ly, he ma ks, lesions, and c yp s appea in he i is in he co esponding egion. Fu he mo e, in Figu e 3i can be clea ly seen he a ia ions in he i is pa e n o colou o igh and le eye i ises. Change in he ma ks and pa e ns on he i ises e lec he a e -e ec s o ch onic kidney disease. 3.2. I is-Acquisi ion Sys em (IAS) The I is-Acquisi ion Sys em (shown in Figu e 4) collec s he da a om di e en subjec s while using a high- esolu ion i is came a along wi h he pa ien ’s de ailed medical his o y and pe o ms s a is ical s udy based on he ch onic kidney p oblem and s o e i in he da a-se . To acqui e he da a he IAS is linked wi h hospi als and medical labs. Du ing i is acquisi ion, he subjec s wi h ch onic kidney disease a e ga he ed and labeled. These labelings a e based on subjec medical epo s and his o y wi h GFR < 30. Figu e 4shows he IAS wo king en i onmen , which acqui es ue RGB colo ed images in high esolu ion. The i is came a uses a cha ge-coupled de ice (CCD) based image senso 24 Megapixel Digi al Single Lens Re lex (DSLR) Canon Came a wi h a 100 mm Mac o lens ha ga he s colo images wi h a esolu ion up o 2560 × 1920 pixels. The mac o lens con igu a ion is con igu ed specially o i idology ha ing ISO se ings be ween 100 o 800 and a na ow ape u e ha esul s in good exposu e. The IAS has a USB in e ace ha can in e ace easily wi h any compu e de ice and allows o da a o a el a an a e age o en imes he speed o he no mal pa allel po . 3.3. P ocessing Sys em The sec ion desc ibes he UCERD GPU-based supe compu e sys em a chi ec u e (shown in Figu e 5), which is used o execu e he ICKIS algo i hm. UCERD GPU-based supe compu e is an In el Xeon p ocesso s ha ing enough p ocessing powe and speed o handle he in ensi e p ocessing and N idia Pascal GPU accele a o s. The UCERD GPU-based supe compu e sys em uses ou nodes, and each node is equipped wi h 12 co es and wo h eads/co es, a o al o 24 h eads, and 2 NVidia-Pascal GPUs. The dis ibu ed nodes a e in e connec ed using Gigabi in e connec ions and hey u ilize he Cen OS Linux Ope a ing Sys em. This sys em has a peak pe o mance o 48.5 Te a loa ing-poin ope a ions pe second. Figu e 4. Pho og aph o I is Acquisi ion Sys em. Symme y 2020,12, 2066 7 o 14 Figu e 5. Pho og aph o UCERD GPU-based Supe compu ing Clus e . 3.4. A i icial In elligence (AI) Algo i hm Figu e 6shows he block diag am o he AI Algo i hm. Py hon 3.6 p og amming language, OpenCV compu e ision, and Tenso -Flow-2 deep lea ning amewo k a e used o p og am he I is-based Ch onic Kidney Iden i ica ion Sys em (ICKIS) algo i hm. Figu e 6. AI algo i hm used o ain and es he da a-se o ch onic kidney p oblem. The con en ional neu al ne wo k (CNN) is used in his esea ch wo k o ex ac and classi y co e ea u es au oma ically while using il e s. These ea u es a e being ex ac ed om he egion o in e es (ROI), excluding o he i is a eas. The size o he il e and weigh s we e au oma ically adjus based on ini ial aining esul s. La e egula iza ion echniques a e being applied, and he weigh s a e adjus ed o ine- uning. I is no ed ha , as he il e s inc ease om 96 o he inpu image, he a ge ed Symme y 2020,12, 2066 8 o 14 pa e ns and ma ks a e no encoded. Addi ionally, he inc ease in il e size does no a ec he accu acy, bu inc eases he ime. On he o he hand, he weigh s o he il e s a e being added and adjus ed based on eal- ime ails esul s. The ea u ed in o ma ion goes h ough he successi e con olu ion il e s and ge s ou inc easingly pu i ied. A he max-pooling s age [ 63 , 64 ], he mos app op ia e ea u es a e ex ac ed, and he ully connec ed laye a he end-s age is used o classi ica ion pu poses. The AI algo i hm has 195,529,698 ainable pa ame e s. The pa ame e iden i ica ion o he ICKIS algo i hm is added by eading om he sou ce and by eal- ime expe imen s-based expe iences. The inpu o he AI algo i hm is aining and es ing da a-se s ha consis o i is images, and each image is labeled wi h heal hy kidney and ch onic kidney classes. The inpu esolu ion o ou da a-se is 2560 × 1920 × 3, he e o e he inpu laye esizes he da a-se in o 640 ×640 ×3 . The AI algo i hm uses h ee con olu ion laye s o ex ac he ea u es o he images while using con olu ion il e s, wi h 2 × 2 max-pooling. The i s con olu ion laye applies 96 il e s wi h a window size o 9 × 9. The pooling laye 1 (Pooling 1) akes he esul s o con olu ion laye 1 (Con 1) (as shown in Figu e 6) and down samples he image o 320 × 320 by using 2 × 2 max-pooling ha ing a s ide o 2. Max pooling pe o ms a educ ion and o e - i ing in dimensions o he image by aking he maximum pixel alue o a g id. La e , he Con 2 [ 65 ] laye applies 64 il e s ha ing a window size o 5 × 5. The Con 3 laye applies 32 il e s wi h a window size o 3 × 3. The ou pu o he con olu ion laye is ou -dimensional; he e o e, we add 2 Fully Connec ed (FC) laye s ha con e he inpu laye in o wo-dimensional. La e , a so -max laye is added wi h each FC laye . Subsequen ly, he ou pu o FC laye s is connec ed wi h he inal laye , which is he so -max laye . The inal laye is used o classi y he image in o one ca ego y ou o wo gi en ca ego ies acco ding o he ea u es o he image. The ou pu o he ICKIS algo i hm has wo classes/ca ego ies ch onic kidney and heal hy kidney. The pa ame e iden i ica ion o he ICKIS algo i hm is added by eading om he sou ce and by eal- ime expe imen s based expe iences. 4. Resul s and Discussion In his sec ion, we pe o m he es ing and alida ion o he p oposed ICKIS sys em and discuss da a-se s, accu acy, and pe o mance o he algo i hm. A e wa ds, we compa e ou ICKIS algo i hm wi h al eady exis ing algo i hms. This sec ion is di ided in o ou subsec ions: he da a-se ; he aining me hodology; he alida ion and accu acy; and, compa ison wi h o he p oposed algo i hms. 4.1. I is Da a-Se The da a we e ga he ed om he IIMCT-Pakis an Railway Hospi al and NIRM-Na ional Ins i u e o Rehabili a ion Medicine. Wi h mo e han wo di e en ins i u es collabo a ing in his s udy, all o he esea ch publica ion e hics and anonymi y o subjec s a e espec ed a all s ages o he p ojec . All pa s o he da a collec ion a e ca ied ou by skilled p o essionals (Biomedical Depa men , Riphah In e na ional Uni e si y). The au ho s ha e ca ied ou mee ings wi h all pa ies and each g oup knows hei ole in he p ojec . Regula mee ings a e held be ween all pa ies o o esee and plan o any issue ha may a ise. A o al o ou housand subjec s a e used in his esea ch, in which 2000 subjec s ha e ch onic kidney disease and he emaining 2000 a e subjec s wi h heal hy kidneys. The ch onic kidney disease subjec s a e in es iga ed h ough hei medical epo s and only hose subjec s a e conside ed ha ha e GFR < 30. Ten images om each subjec a e aken wi h i e images o he le eye and i e images o he igh eyes. Each i is image has a esolu ion o 2560 ×1920 ×3 pixels. The da a-se (as shown in Table 1) holds 40,000 i is images o bo h le and igh i is, ou o which 20,000 images belong o heal hy subjec s, whe eas he emaining 20,000 images belong o subjec s ha ing ch onic kidney disease. The da a-se s a e u he ca ego ized in o T aining Da a-Se and Tes ing Da a-Se . Each Da a-Se holds 20,000 i is images, ou o which 50% images comp ise subjec s wi h ch onic kidney disease, and he emaining 50% images a e o heal hy subjec s. Symme y 2020,12, 2066 9 o 14 Table 1. T aining and Tes ing Da a-Se . Da a-Se Heal hy Ch onic To al Subjec s/Images Subjec s/Images Subjec s/Images T aining 1000/10,000 1000/10,000 2000/20,000 Tes ing 1000/10,000 1000/10,000 2000/20,000 To al 2000/20,000 2000/20,000 4000/40,000 4.2. T aining Me hodology In his sec ion, we explain how he ICKIS AI algo i hm (shown in Figu e 6) is ained on T aining Da a-Se (Table 1) and UCERD-GPU based supe compu e . The AI algo i hm has 195,529,698 ainable pa ame e s. In o de o ain he AI algo i hm, we applied da a-le el pa allelism ha di ides he aining da a in o mul iple subse s, each se is execu ed on a sepa a e node. To pe o m synch oniza ion, he S ochas ic G adien Descen (SGD) is applied ha inco po a es he esul o each compu a ion wi h he AI algo i hm. We ha e applied aining o he ICKIS AI algo i hm wi h s ochas ic g adien descen by u ilizing he Ho owa d [ 66 ] deep lea ning oolki . The dis ibu ed execu ion on a supe compu e sys em uses eigh eplicas each pe o ming p ocessing on an NVidia Pascal GPU. The aining me hodology on he UCERD-GPU based supe compu e akes 94 min. o ain he ICKIS AI algo i hm. The aining me hodology p o ides a as , scalable, in elligen , and high-pe o mance compu ing based me hod o he p ima y diagnosing o ch onic kidney disease. 4.3. Valida ion and Accu acy The es ing da a-se (in Table 1) is used in o de o alida e he ICKIS. Because each subjec has 10 images, he subjec is conside ed o be a ch onic kidney pa ien i he AI algo i hm has ch onic kidney pa e ns in 5 o mo e o i s i is images. I he ICKIS AI algo i hm de ec s ch onic kidney p oblems ei he in he igh o le i is o he subjec , i is conside ed o ha e ch onic kidney disease. In o de o ecognize he heal hy kidneys, images o bo h eyes mus ha e a heal hy kidney unc ion, so he subjec is conside ed o be heal hy i he AI algo i hm has heal hy kidney pa e ns o all o i s en i is images. The esul s o he es subjec s (1000 subjec s wi h ch onic kidney p oblems and 1000 subjec s wi h heal hy kidneys) in Table 2, espec i ely, show he co ec classi ica ion, namely subjec s wi h heal hy kidney and ch onic kidney is 95.4% and 98.2%, espec i ely. On he o he hand, he alse classi ica ions o subjec s wi h a heal hy kidney a e 4.6%. False-nega i e classi ica ion is conside ed o be one o he mos impo an ac o s in he pe o mance o he designed classi ie , and i is ound o be 1.8%. This pe cen age indica es ha , ou o 100 subjec s wi h ch onic kidney p oblems, almos wo subjec s may be misdiagnosed. Table 2. A i icial In elligence (AI) Algo i hm ou comes o he es ing da a-se o heal hy and ch onic kidney. Heal hy Kidney Ch onic Kidney Numbe o es ed Subjec s 1000 1000 Co ec Classi ied 954 982 False Classi ied 46 18 The ICKIS AI algo i hm is es ed on a sepa a e Tes ing Se (shown in Table 1). The esul s show (Figu e 7) he accu acy o he ICKIS AI algo i hm. The esul shows ha he algo i hm iden i ies he ch onic kidney subjec wi h an accu acy o 96.8%. The ICKIS algo i hm iden i ies he imaging esul s o he i is scan ha ing a iable and non-speci ic pa e ns le el o kidney p oblem. The algo i hm can also e eal he e olu ion o kidney p oblems wi h he ime and e ec o medica ion on he pa ien . The c ea inine es o he subjec s e i ies he esul s.