An accurate Alzheimer disease diagnosis approach based on samples balanced genetic algorithm and extreme learning machine using MRI.

Transcription

1 Curr Neurobiol 2016; 7 (3): ISSN An accurae Alzheimer disease diagnosis approach based on samples balanced geneic algorihm and exreme learning machine using MRI. Vasily Sachnev* School of Informaion, Communicaion and Elecronics Engineering, Caholic Universiy, Bucheon, Korea Absc A Samples Balanced Geneic Algorihm and Exreme Learning Machine (SBGA-ELM) designed for accurae Alzheimer Disease diagnosis and idenifying biomarers associaed wih AD is presened in his paper. Proposed Alzheimer Disease diagnosis approach uses se of MRI of OASIS public daabase o build an efficien AD classifier. Proposed Alzheimer Disease diagnosis approach conains 2 seps: 1) voxels selecion based on Samples Balanced Geneic Algorihm (SBGA), and 2) AD classificaion based on Exreme Learning Machine (ELM). In a firs sep voxels selecion sep a subse of voxels wih promising properies for AD diagnosis is seleced from a complee se of voxels exced from OASIS daa base. A selecion process is exremely complex and requires specifically designed echnique. In his paper, we propose a Samples Balanced Geneic Algorihm (SBGA) for searching a subse of voxels among voxels from OASIS daabase. In a second AD classificaion sep, a discovered subse of voxels is used o consruc an efficien AD classifier based on Exreme Learning Machine (ELM). A discovered subse of voxels eeps high generalizaion performances of AD classificaion using ELM in various scenarios and highlighs imporance of he chosen voxels for AD research. AD classifier wih maximum classificaion accuracy creaed using he bes se of chosen voxels is our final AD diagnosis approach and he bes se of chosen voxels is poenial AD biomarers. Experimens wih proposed SBGA-ELM show an average ing accuracy 87%. Experimens clearly indicae he efficiency of he proposed SBGA-ELM for AD diagnosis and highligh improvemen over exising echniques. Keywords: Alzheimer disease, OASIS, Samples balanced geneic algorihm, Exreme learning machine. Acceped on December 21, 2016 Inroducion Alzheimer's disease (AD) is a case of demenia, or brain's neurons disorder wih various sympoms, lie memory loss, confusion and learning problems. AD is more common in older individuals. Alzheimer's disease diagnosis, especially in he early sages, is he one of he mos challenging problems in medicine. AD diagnosis in he early sages using simple and progressive mehods can be a signal o sar reamen ha finally may slow down he disease. Hence, new more advanced AD diagnosis in early sages is needed. Brain imaging is he one of he mos powerful ools for Alzheimer's disease diagnosis and AD research. Brain imaging invesigaes human brain by visualizing brain issues using differen progressive mehods. Several brain imaging echniques for diagnosing and research are used nowadays: 1) CT (Compued Tomography); 2) SPECT (Single-Phoon Emission Compued Tomography, 90 3) PET (Posiron Emission Tomography) and MRI (Magneic Resonance Imaging). Compared o recen wors Alzheimer's disease diagnosis using CT (Compued Tomography) [1-3] is no accurae enough o deec AD. Boh, SPECT and PET use radioacive isoopes for ing [4,5], and become harmful for paiens afer several s. Colleced by SPECT and PET daa may no be sufficien for proper AD diagnosis. SPECT has a low spaial resoluion, which causes lower accuracy for AD diagnosis. Compared o CT, SPECT and PET, Magneic Resonance Imaging (MRI) is an efficien and fas brain imaging ool wih high spaial resoluion of brain shape and volume enough for accurae AD diagnosis. MRI is also able o process dynamic analysis of he brain shape and volume. Dynamic analysis deecs fas modificaion in he brain, which is exremely imporan for AD diagnosis [6-8]. MRI is widely used for accurae Alzheimer's disease in early sages. Analysis of he MRI aen from AD paiens

2 An accurae Alzheimer disease diagnosis approach based on samples balanced geneic algorihm and exreme learning machine using MRI. in early sages defines modificaions in hippocampus and enorhinal corex brain areas [9,10]. The auhors presened mehods o differeniae AD paiens and normal persons by examining volume of he hippocampus and enorhinal corex in he brain areas chosen manually. Such manual mehod is no always accurae, depends on he researcher and may cause misaes. Hence, an auomaic approach is highly required. Auomaic AD diagnosis approaches use feaures exced from MRI and diional machine learning echniques for accurae AD classificaion [6-8]. AD diagnosis is possible due o arificial changes in he brain's volume capured by MRI. MRI analysis based on ROI (Regions-of-ineres) idenifies brain areas responsible for AD and numerically esimaes discovered brain areas for furher analysis. Chupin e al. [8] presened an ROI based approach for AD diagnosis, which auomaically segmens hippocampus areas using probabilisic and anaomical mehods. ROI based AD diagnosis is simple in naure and may efficienly deec AD paiens, when issue loss in he brain is significan. However, ROI based AD diagnosis is failing, when issue loss in he brain is small. Hence, he new brain esimaion mehodology differen from he ROI need o be developed. A suiable esimaion mehodology based on MRI has been invened by Ashburner and Frison [11]. The auhors proposed whole brain morphomeric esimaion for accurae AD deecion, even if issues loss is relaively small. Proposed in [11] brain morphomeric esimaion idenifies and measures modificaion of issue volume in individual brains or beween he brains of normal and abnormal persons. Brain morphomeric esimaion creaes a se of relevan morphomeric feaures for accurae AD diagnosis. Exced morphomeric feaures or voxels represen probabiliies of he gray, whie maers and cerebrospinal fluid issues. Kloppel e al. [6] and Davazios e al. [7] ined Suppor Vecor Machine (SVM) classifier for AD diagnosis using a se of morphomeric feaures. Proposed SBGA-ELM uilizes a se of morphomeric feaures o build AD classifier and process experimens. Mahanand e al. [12] exploied MRI from public Open Access Series of Imaging Sudies (OASIS) [13] daabase o build a se of 5788 feaures exced using Voxel-Based Morphomery (VBM) approach. The auhors reduced se of feaures using principal componen analysis. The reduced se of feaures was used o build AD classifier using a Self-adapive Resource Allocaion Newor (SRAN). Experimens wih 30 normal persons and 30 AD paiens from he OASIS daabase clearly indicae ha discovered a reduced se of feaures is sufficien for accurae AD diagnosis. Alhough, Suppor Vecor Machine (SVM) is a popular and efficien machine learning ool for solving various classificaion problems (including AD classificaion problem [6,7]), ining phase of SVM is compuaionally exensive and usually needs a significan ime o build a classifier wih high classificaion accuracy. Hence, a new machine learning echnique wih accepable classificaion accuracy and fas ining phase is needed. A se of morphomeric feaures exced from MRI of OASIS daa is mosly redundan for analysis. Thus, direc use of he complee se of exced morphomeric feaures o build an AD classifier does no guaranee high classificaion performance. Hence, search for a reduced se of voxels (or feaures) which creaes an AD classifier wih beer classificaion accuracy is needed. Search for a reduced se of feaures wih promising properies is one of he mos common opimizaion problems in many research areas of science. Saraswahi e al. [14] searched for a reduced se of genes from popular Global Cancer Map (GCM) o build a cancer diagnosis approach using Ineger Coded Geneic Algorihm and Paricle Swarm Opimizaion coupled o Exreme Learning Machine (ICGA-PSO-ELM). This mehod has a serious drawbac; he number of genes has o be assigned manually. Presened cancer classifier deecs 14 ypes of cancer wih high classificaion accuracy. Sachnev e al. [15] used Binary Coded Geneic Algorihm for searching an opimal se of genes from GCM daabase. The auhors repored abou 52 discovered biomarers from he se of 92 chosen genes, which were used o build a cancer classifier. An efficien AD diagnosis approach based on proposed Sample Balanced Geneic Algorihm coupled o Exreme Learning Machine (SBGA-ELM) is proposed in his paper. Proposed Sample Balanced Geneic Algorihm is a compleely auomaic approach for searching a robus se of voxels and building a classifier for accurae AD diagnosis. Sample Balanced Geneic Algorihm uses wo proposed crossovers designed specifically for AD daa from OASIS daa base. Proposed Regular Sample Balanced Crossover, Irregular Sample Balanced Crossover and Sample Balanced Muaion creae a basis for proposed SBGA. A reduced se of robus voxels is used o in AD classifier based on Exreme Learning Machine. ELM classifier wih maximum classificaion accuracy is our final AD diagnosis approach. A reduced se of robus voxels, which creaes he bes ELM classifier, is used o discover biomarers responsible for AD. Proposed paper is organized as follows: In Secion II an OASIS daabase is presened. Secion III presens a framewor of he proposed SBGA-ELM in deails. Experimenal resuls are presened in Secion IV. Secion V concludes a paper. Oasis Publicly available Open Access Series of Imaging Sudies (OASIS) daa se [13] is a famous daabase of MRI for Alzheimer s disease research. OASIS conains MRI from wo ses of daa: 218 persons years old, and 198 persons years old. A se of 198 older persons 91

3 Sachnev is used in his paper for analysis. 98 of 198 are normal persons wihou AD. Clinical Demenia Raing (CDR) of 98 paiens is 0. The res 100 are AD paiens; 70 persons are very mild AD paiens wih CDR=0.5; 28 persons are mild AD paiens wih CDR=1. MRI from he OASIS daabase has been creaed using Siemens 1.5-T vision scanner in a single imaging session. T1-weighed 3D MPRAGE (Magneizaion-prepared Rapid acquisiion Gradien Echo) daa ses of he whole brain were acquired. The acquired volumes had 128 sagial 1.25 mm slices wihou gaps and a pixel resoluion of (1 1 mm). Finally, he brain morphomeric esimaion has been used o exc se of voxels (feaures) from MRI of OASIS daabase. Thus, presened Alzheimer disease classificaion problem is a binary classificaion problem wih feaures and 198 samples: 100 AD paiens and 98 normal persons. Proposed SBGA-ELM processes presened OASIS daa and builds an efficien AD classifier. Deail explanaion of he proposed SBGA-ELM is presened below. Proposed SBGA-ELM Approach for AD Diagnosis Proposed Sample Balanced Geneic Algorihm coupled wih Exreme Learning Machine uilizes feaures exced from MRI of OASIS daabase o creae an accurae AD diagnosis approach. Proposed SBGA-ELM conains 2 major seps: 1) Voxels selecion and 2) AD classificaion (Figure 1). Proposed SBGA-ELM sars from processing MRI in OASIS daa (See voxels selecion procedure in Figure 1). Each MRI from OASIS daabase is convered o he se of voxels using brain morphomeric esimaion. Voxels selecion procedure iniiaes a search for a reduced se of voxels based on proposed Sample Balanced Geneic Algorihm. Each reduced se of voxels is used hen o build an AD classifier using Exreme Learning Machine. In he proposed mehod boh, Sample Balanced Geneic Algorihm and Exreme Learning Machine, creaes an efficien unified framewor. The efficiency of he creaed ELM classifier is mosly depending on se of reduced voxels chosen by SBGA. Recen AD research focus on discovering se of voxels (or biomarers) probably responsible for AD. We assume ha he subses of voxels slighly differen compared o se of biomarers creae AD classifiers wih accuracies closed o maximum. Thus, sligh modificaions in se of reduced voxels chosen by SBGA may eiher improve he accuracy of he AD classificaion or degrade. Ses of voxels, which cause improvemens, can be modified again and again unil no improvemen is reached. Such ieraive segy is a basis for Geneic Algorihm. Sample balanced geneic algorihm (SBGA) Proposed Sample Balanced Geneic Algorihm is a modificaion of well-nown Geneic Algorihm adaped for searching he bes reduced se of voxels suiable o creae an AD classifier wih maximum classificaion accuracy. Geneic Algorihm (GA) is a famous opimizaion ool for solving complex opimizaion problems in many areas of science and engineering. GA explois self-adaped, mosly random, gene recombinaion mechanism from naure. In GA each opimizaion problem is specified by he se of chromosomes. Manipulaions wih chromosomes exced from an opimizaion problem helps GA o search for an opimal soluion. Proposed Samples Balanced Geneic Algorihm uses a sring of binary coefficiens as a se of meaningful chromosomes. Sring of binary coefficiens: As was described before, given opimizaion problem of searching an opimal se of voxels from OASIS daabase has o be nsformed o a se of relevan chromosomes. Each chromosome represens value criical for a given opimizaion problem. The se of exced chromosomes represens a soluion of he given opimizaion problem. Manipulaions wih chromosomes may eiher improve or degrade a given opimizaion problem. Finess funcion uses soluion (or se of chromosomes) o calculae a numerical measure of he given problem. The calculaed numerical measure is a finess value, which is used o evaluae each soluion in GA framewor. In he proposed AD diagnosis approach each voxel from OASIS daabase represens a chromosome for he proposed GA framewor. The OASIS daabase conains voxels. Then, each soluion for he AD classificaion problem conains chromosomes. Proposed Sample Balanced Geneic Algorihm searches a reduced se of voxels/chromosomes from voxels/chromosomes Figure 1. The framewor of he proposed AD diagnosis based on SBGA-ELM. 92

4 An accurae Alzheimer disease diagnosis approach based on samples balanced geneic algorihm and exreme learning machine using MRI. available on OASIS daa base. In his research, each chromosome assigns an appearance saus of he corresponding voxel from OASIS daabase. According o a chromosome value each voxel can be eiher piced or no o build a reduced se of voxels. Thus, he soluion for he proposed SBGA is a se of values: pics/notpic, or rue / false, or binary 1 / 0 (Figure 2). In he proposed mehod binary value 1 highlighs chosen voxels, binary values 0 highlighs sipped voxels. Finally, a proposed sring of binary coefficiens (or binary soluion) builds a reduced se of voxels for furher analysis (Figure 2). Proposed Samples Balanced Geneic Algorihm creaes new binary soluions by using 3 proposed geneic operaors: Regular Sample Balanced Crossover, Irregular Sample Balanced Crossover and Sample Balanced Muaion. Geneic operaors: Geneic Algorihm uses crossover and muaion o creae new soluions (Figure 3). Geneic operaors manipulae wih chromosomes of given opimizaion problem similar o chromosome exchange mechanism from naure. Crossover creaes new genome by exchanging geneic maerials from wo inpu sources (genomes). Creaed genome may conain properies from boh inpu sources. Such gene recombinaion is chaoic and unprediced in naure. Crossover achieves properies exchange in beween inpu and oupu. Such properies may be eiher enhanced or degraded. Muaion modifies genes randomly and usually causes significan properies degradaions. Someimes muaion causes new properies, which do no exis in inpu genomes. Geneic Operaors in Geneic Algorihm process chromosome exchange procedure in beween inpu sources similar o crossover and muaion of naure. GA crossover recombines chromosomes from 2 randomly chosen soluions and builds a new soluion. GA crossover always follows fix segy o build new soluions. Efficiency of he Geneic Algorihm depends on problem specificaion, he efficiency of he chosen geneic operaors and Geneic Algorihm seings. Problem specificaion deals wih a proper way of nsforming given opimizaion problem o he se of relevan chromosomes. Wrong choice of he problem specificaion leads o incorrec wor of whole GA and failing. The efficiency of he chosen geneic operaors mosly depends on giving opimizaion problem and daa. Differen opimizaion problems may need a differen crossover and muaion, or heir combinaion. GA seings affec main procedures of GA and cause various problems. Incorrec seings damage convergence of he GA, creae disbalance in beween populaions and, finally, fail GA. Figure 2. Binary soluion for AD diagnosis based on SBGA-ELM. Figure 3. Regular Samples Balanced Crossover. 93

5 Sachnev The proper choice of he crossover for GA is a big challenge. Wrong choice significanly damages he generalizaion abiliy of he GA. The concep of hybrid crossover may solve such problem. The hybrid crossover provides a choice in beween several crossovers in a pool. A pool may conain few well-nown crossovers, few crossovers wih novel design, or even special crossovers. Hybrid crossover randomly pics one crossover from he pool and processes i o generae new soluion. Thus, final se of soluions is creaed by using all crossovers lised in a pool. Hybrid crossover is efficien if opimizaion problem is relaively new and proper crossover is difficul o choose. Someimes a hybrid crossover is useful when single crossover does no guaranee efficien search and combinaion of few crossovers is needed. Search for an opimal se of voxels for he AD classificaion problem using GA based on various exised crossovers including hybrid crossover fails. Hence, new crossover or a se of crossovers designed specifically for he AD classificaion problem using he GA framewor is needed. In his paper wo new crossovers and one muaion designed specifically for GA focused on solving AD classificaion problem are presened. Regular Sample Balanced Crossover, Irregular Sample Balanced Crossover and Sample Balanced Muaion are presened in his paper. GA based on well-nown crossovers fails because of problem specificaion based on he binary sring wih coefficiens for a given AD classificaion problem. Each binary soluion conains binary coefficiens. The number of binary coefficiens 1 is always relaively small. Each binary soluion conains in average binary coefficiens 1, res coefficiens are 0. According o problem specificaion only binary coefficiens 1 are meaningful and define he efficiency of he GA. All examined crossovers significanly modify he number of binary coefficiens 1 in he new soluion compared o he number of coefficiens 1 in inpu soluions, which causes significan performance loss for AD classificaion. In he wors case scenario crossovers creaes soluions wih all zeros, which means here is no chosen feaures o build a classifier. This case is unaccepable. Hence, new Regular Sample Balanced Crossover, Irregular Sample Balanced Crossover and Sample Balanced Muaion are proposed o handle a challenge. Regular Samples Balanced Crossover manages a challenge by conrolling a number of binary coefficiens 1 in a new soluion compared o a number of binary coefficiens 1 in inpu soluions. Regular Samples Balanced Crossover is displayed in (Figure 3). Proposed Regular Samples Balanced Crossover exchanges chromosomes from soluion #1 and soluion #2 o build a new soluion (Figure 3). Regular Samples Balanced Crossover eeps binary coefficiens 1 locaed in boh inpu soluions, and randomly pics binary coefficiens 1 differen in boh inpu soluions. Proposed Regular Samples Balanced Crossover collecs locaions of he binary coefficiens 1 in boh inpu soluions (see indexes #1 and indexes #2 in Figure 3). Then exced indexes are divided ino he se of differen indexes and same indexes. Same indexes are direcly moved o he se of new indexes, which creaes a New soluion. Differen indexes are divided randomly by Random choice according o random parameer S r. Seleced indexes are hen unified wih same indexes and build new soluion Random parameer S r in Random choice is in he range of Random parameer is randomly generaed every ime when GA calls for crossover. Figure 3 presens an example of he proposed Regular Samples Balanced Crossover. In he given example, wo inpu soluions ( soluion #1 and soluion #2 ) are given, random parameer S r is equal 0.6. Binary coefficiens 1 are locaed in he posiions {1, 3, 5, 7, 10} and {1, 2, 7, 8, 10} of soluion #1 and soluion #2 respecively (see index #1 and index #2 ). Then, exced indexes are divided ino same indexes {1, 3, 7} and differen indexes {2, 5, 8, 10, 11}. Random choice randomly pics L S r indexes from differen indexes, where is he number of indexes in he se differen indexes. Then, L S r = =3. Random choice pics indexes {2, 8, 10}. Then, new indexes {1, 2, 7, 8, 10} unifies same indexes {1, 3, 7} and indexes chosen by Random choice {2, 8, 10}. Finally, new indexes creaes new soluion (Figure 3). Proposed Regular Sample Balanced Crossover eeps he balance of he binary coefficiens 1 in beween inpu and oupu soluions and efficienly processes any binary soluions for he AD classificaion problem. If he number of common binary coefficiens 1 is relaively small, he size of he se same indexes is small or even 0 (here are no same indexes in he inpu soluions). The new soluion is creaed mosly from differen indexes piced randomly. In his case new soluion and boh inpu soluions have maximum possible difference. Such soluions may eiher degrade properies significanly or significanly improve. If he number of common binary coefficiens from inpu soluions is significan, he mos of he indexes from boh inpu soluions belong o same indexes. In his case new soluion is mosly creaed from same indexes. Thus, proposed Regular Samples Balanced Crossover balances GA in various scenarios. In he beginning GA manages soluions generaed randomly, and inpu soluions have a small number of binary 1 in common. Then new soluions are creaed from differen indexes, which is similar o chaoic search. A he end, GA already idenifies a se of common locaions of binary 1, mos of he indexes from inpu soluions belong o "same indexes" se, and new soluions are slighly modified compared o inpu soluions, which is similar o searching an opimal soluion. Irregular Samples Balanced Crossover is a modified 94

6 An accurae Alzheimer disease diagnosis approach based on samples balanced geneic algorihm and exreme learning machine using MRI. version of he Regular Sample Balanced Crossover presened above. Irregular Samples Balanced Crossover creaes new soluions from randomly piced indexes from differen indexes and same indexes (Figure 4). Spli parameer for Spli differen indexes is S dif in he range of ; spli parameer Spli differen indexes is S same in he range Spli parameers S dif and S same are randomly generaed every ime when GA calls for crossover. In he example presened in Figure 4 S dif and S same are 0.6 and 0.8, respecively. Irregular Samples Balanced Crossover presened in Fig. 4 randomly pics L dif S dif = =3 from differen indexes and L same S same = =2 from same indexes. Thus, new index {1, 2, 7, 8, 10} unifies 3 indexes from differen indexes {2, 8, 10} and 2 indexes form same indexes {1, 7}. Irregular Samples Balanced Crossover is needed when GA has almos finished searching and significan porion of he generaed soluions conain same indexes. In case when soluion #1 and soluion #2 are he same, Regular Sample Balanced Crossover generaes new soluion exacly he same as soluion #1 and soluion #2. Irregular Sample Balanced Crossover does no have his drawbac. In he previous scenario Irregular Sample Balanced Crossover generaes new soluion differen from soluion #1 and soluion #2. In his research he concep of hybrid crossover is implemened. Hybrid crossover randomly pics eiher Regular Sample Balanced Crossover or Irregular Sample Balanced Crossover. Regular Sample Balanced Crossover is efficien excep cases when soluion #1 and soluion #2 are he same. Irregular Sample Balanced Crossover efficienly handles cases when soluion #1 and soluion #2 are he same and does no degrade GA performance significanly oherwise. Proposed Samples Balanced Muaion randomly modifies binary coefficiens from an inpu soluion such ha he number of binary 1 in new soluion and an inpu soluion eeps same. The finess funcion is a special procedure o evaluae soluions in GA. Finess funcion creaes a finess value, which numerically esimaes an imporance of each soluion in GA framewor. Soluions wih maximum or minimum finess value (depending on wha ind of opimal soluion is a arge for given opimizaion problem) are opimal or subopimal soluions for a given problem. Finess values are used o sor examined he soluions in GA framewor. In GA all imporan soluions wih finess values closer o maxima or minima are used o generae slighly differen soluions wih even beer finess values. Non imporan soluions are sipped. In he proposed SBGA-ELM framewor finess funcion is an AD classifier based on Exreme Learning Machine. AD classifier is ined using a reduced se of voxels specified by curren sring of binary coefficiens or soluion. Given se of voxels is used o in 10 ELM classifiers using random parameers. Average ing accuracy of 10 creaed ELM classifiers is a finess value. Selecion procedure sors given soluions according o finess values and selecs soluions wih promising finess values for furher processing in GA framewor. Selecion procedure assigns probabiliy o each soluion according o is finess value. Thus, soluion wih significan finess value has a beer chance o survive and generae anoher soluion in GA. Soluions wih insignifican finess values are ignored. In he proposed SBGA-ELM geomeric raning mehod [16] is used as a selecion procedure. Geomeric raning mehod sors all given soluions in descending order according o is finess value and assigns a probabiliy o each soluion P j as follows: r 1 P = q 1 q (1) j ( ) j where q q = 1 1 q ( ) N q' is selecion probabiliy, r j is a ran of j-h soluion in he parially ordered se, and N is he populaion size. The deail explanaion of he geomeric raning mehod is given in [16]. In his research parameer q=10-3. Figure 4. Irregular samples balanced crossover. 95

7 Sachnev Terminaion crieria: Geneic Algorihm sops when new generaions no longer produce beer resul during las 50 generaions. SBGA-ELM framewor. SBGA-ELM processes iniializaion sep and several generaions (Figure 5). Each generaion conains selecion procedure, se of geneic operaors and finess funcion. Proposed Sample Balanced Geneic Algorihm sars from iniializaion sep. 200 binary soluions are generaed randomly. Each soluion is hen processed by finess funcion and finess value is calculaed. Each soluion is a se of binary coefficiens. The number of binary 1 in each iniial soluion is limied o he range of Then, each iniial soluion builds a reduced se of voxels from OASIS daa based (Figure 2). Reduced se of voxels is used o in 10 AD classifier based on Exreme Learning Machine. Creaed ELM classifier is a finess funcion. Average overall ing accuracy is a finess value. Thus, combinaion of 200 iniial binary soluions { F , 200} and corresponding finess f 0, f 0, f 0,, f 0 builds iniial populaion of he values { } GA F 0 (see Figure 5). Each n-h generaion in SBGA sars from selecion procedure based on he geomeric raning mehod. Selecion procedure pics soluions from previous populaion n 1 F according o assigned probabiliies. Pair of seleced binary soluions is processed by geneic operaors. Hybrid crossover based on Regular Sample Balanced Crossover and Irregular Sample Balanced Crossover, and Sample Balanced muaion creaes a se of new binary Figure 5. Framewor of he proposed SBGA-ELM. soluions. { F n n n n 1 2 3, 200} Similar o he iniializaion sep each binary soluion F n i is evaluaed by finess funcion and finess value f i is calculaed. Combinaion of all binary soluions { F n n n, n } and corresponding finess values { F n n n n 1 2 3, 200} builds n-h populaion F n. In each generaion crossover creaes 70% or 140 new soluions, muaion creaes res 30% or 60 new soluions. GA processes generaions unil new generaion no longer produces beer resul during las 50 generaions. Exreme Learning Machine (ELM) Exreme Learning Machine is a machine learning echnique wih exremely fas learning phase and good generalizaion performances. Technically ELM is a single hidden layer feedforward neural newor where inpu weighs and bias of he hidden neurons are randomly assigned and oupu weighs are esimaed analyically [11]. ELM parameers such as he number of hidden neurons, inpu weighs and bias of he hidden neurons can be calculaed using geneic algorihm [17] or mea-cogniive approach [18]. In he proposed AD diagnosis approach Exreme Learning Machine solves AD classificaion problem. The AD classificaion problem is he binary classificaion problem wih 198 samples (100 AD paiens and 98 normal persons) and dimensional feaures space. In he proposed AD diagnosis approach SBGA significanly reduces feaure space, which simplifies AD classifier based on Exreme Leaning Machine. 96

8 An accurae Alzheimer disease diagnosis approach based on samples balanced geneic algorihm and exreme learning machine using MRI. The framewor o build ELM classifier conrolled by Gaussian hidden neurons in AD classificaion problem is presened below [19-21]. Daa Training and ing ses are creaed using given binary soluion and OASIS daa base. Firs, 198 samples (100 AD, 98 normal) from OASIS are randomly divided ino a ining se (70 AD, 68 normal) and ing se (30 AD, 30 normal) feaures from OASIS daabase is reduced by SBGA o he se of m feaures. Then, he ining se conains 138 samples, i.e., ( 1 1 ) { X, c,, ( X, c ) ,, ( X, c )}; ing se conains 60 samples, i.e., {( X 1, c 1 ),, ( X, c ),, ( X 60, c 60 )} Where X and X are m-dimensional feaure vecor and c { 1, 2} or {AD paien, normal person} is class label. The coded class label y is calculaed as follows: 1, if AD pacien y = 1, if normal person Then y { y,y,, y } { } = and y = y,y,, y are vecors wih all coded class labels for ing and ining. Framewor of he Exreme Learning Machine is summarized as follows: Training phase: 1) Assign a number of hidden neurons L. (2) 2) Generae ses of inpu weighs A mxl and widh (bias) b Lx1 of hidden Gaussian neurons randomly. 3) Compue he hidden layer oupu marix G 1 N g1 g 1 G = 1 N gl g L where N is a number of samples (N=138 for ining and N=60 for ing) g j is a response of j-h hidden neuron for -h sample calculaed as follows: g T ( X Aj ) ( X Aj ) j 2 2b j (3) = exp (4) 4) Compue oupu weighs β. â y G = (5) where is a Moore-Penrose generalizaion inverse. Compue prediced coded class labels ŷ yˆ = â G (6) 5) Define prediced class label as follows: cˆ Tesing Phase 1, if yˆ 0 = 2, if ŷ < 0 1) Compue he hidden layer oupu marix G for ing (N = 60) using Equaion 3, where g T ( X Aj ) ( X Aj ) = exp j 2 2b j Compue prediced coded class labels ŷ = â G ŷ 2) Define prediced class label for ing as follows: cˆ 1, if yˆ 0 = 2, if yˆ < 0 The accuracy of any ELM classifier mosly depends on he randomly chosen weighs and hidden neuron bias. In he proposed SBGA-ELM each binary soluion is used o creae 10 ELM classifiers wih randomly generaed weighs and bias. Finess value is an average of 10 overall ing accuracies from 10 creaed ELM classifiers. 10- fold validaion balances generalizaion performance of he ELM and neglec effec of randomness. Experimenal Resuls Experimens wih proposed SBGA-ELM include a ining AD classifier using Exreme Learning Machine and se of 138 samples (70 AD paiens, 68 normal persons), ing using 60 samples (30 AD paiens, 30 normal persons). Experimens also include searching for a reduced se of voxels from OASIS daabase wih a maximum finess value, i.e., maximum overall ing accuracy of he creaed ELM classifier. Proposed SBGA-ELM processed 122 generaions unil erminaion crieria was saisfied, i.e., no improvemen since 72h generaion. Proposed SBGA-ELM has found se of 38 voxels, which were used o creae ELM classifier for AD diagnosis. 10 creaed ELM classifiers for 10-fold validaion show 93% of average ining accuracy and 87% of average ing accuracy (Table 1). Comparison wih exising mehods Table 1. Training and ing accuracies of 10 ELM classifiers wih he bes resuls. Training Tesing Maximum accuracy (%) Maximum accuracy (%) Minimum accuracy (%) Minimum accuracy (%) STD 2.79 STD 3.01 Average accuracy (%) Average accuracy (%)

9 Sachnev Comparison wih exising echniques should cover compleely auomaic AD diagnosis based on machine learning echniques and OASIS daa base. Direc comparison is possible, if examined mehods use a complee se of samples (198 paiens) from OASIS daabase and he complee se of voxels. Unforunaely, researchers mosly use manually reduced se of voxels and samples. Thus, direc comparison is no possible. Kloppel e al. [6] used 4 ses of paiens wih AD and healhy conrols for cross daa base analysis. Each se unifies MRI creaed using one scanner wih fixed seings. The auhors used Suppor Vecor Machine o creae AD classifier. The auhors repored abou 81-96% of he classificaion accuracy for differen scenarios. Davazios e al. [7] colleced MRI from 30 AD paiens and 20 normal persons, exced morphomeric feaures and ined AD classifier based on Suppor Vecor Machine (SVM) classifier. The auhors repored abou 90% of classificaion accuracy. Mahanand e al. [12] used se of MRI from 30 AD paiens and 30 normal from OASIS daabase, build a se of 5788 feaures exced using Voxel-Based Morphomery (VBM) approach. Reduced se of 20 feaures was used o build AD classifier using a Self-adapive Resource Allocaion Newor (SRAN). The auhors repored abou 91% of overall ing accuracy. As was menioned before direc comparison is no valid for all examined exising echniques. Besides, proposed SBGA-ELM uses complee se of samples and voxels for accurae AD diagnosis. Experimens wih complee se of samples and voxels are more challenging. I increases he complexiy of he classifiers and reduces accuracy. A proposed AD diagnosis approach based on SBGA-ELM solved more complex classificaion problem and showed accepable classificaion accuracy. Conclusion In his paper a Sample Balanced Geneic Algorihm coupled wih Exreme learning Machine has been used o creae an efficien AD diagnosis approach. Hybrid crossover based on proposed Regular Sample Balanced Crossover and Irregular Sample Balanced crossover holds convergence of he proposed SBGA-ELM and helps o find a reduced se of voxels wih highes finess value. Experimenal resuls clearly indicae he efficiency of he proposed AD diagnosis approach based on SBGA-ELM. Proposed mehod shows 87% of overall ing accuracy by ing all samples available a he OASIS daa base. Acnowledgemen This wor was suppored by Caholic Universiy of Korea, Research Funds References 1. Thompson PM, Hayashi KM, Duon RA, e al. Tracing Alzheimers disease. Ann N Y Acad Sci 2007; 1097: Lopez OL, Becer JT, Jungreis CA, e al. Compued omography-bu no magneic resonance imagingidenified perivenricular whie-maer lesions predic sympomaic cerebrovascular disease in probable Alzheimer s disease. Arch Neurol 1995; 52: Jobs KA, Barneson LP, Shepsone BJ. Accurae predicion of hisologically confirmed Alzheimer s disease and he differenial diagnosis of demenia: The use of NINCDS-ADRDA and DSM-III-R crieria, SPECT, X-ray CT, and apo E4 in medial emporal lobe demenias. Oxford projec o invesigae memory and aging. In Psychogeriar 1998; 10: Ramrez J, Gorriz JM, Lopez M, e al. Early deecion of he Alzheimer s disease combining feaure selecion and ernel machines. Adv Neuro-Informa Proc 2009; 5507: Lopez M, Ramrez J, Gorriz JM, e al. Principal componen analysis-based echniques and supervised classificaion schemes for he early deecion of Alzheimer s disease. Neurocompuing 2011; 74: Kloppel S, Sonningon CM, Chu C, e al. Auomaic classificaion of MR scans in Alzheimer s disease. Brain 2008; 131: Davazios Can Y, Wu X, e al. Deecion of prodromal Alzheimer s disease via paern classificaion of MRI. Neurobiol Aging 2003; 29: Jac Jr CR, Peersen RC, Xu YC, e al. Predicion of AD wih MRI-based hippocampal volume in mildcogniive impairmen. Neurology 1999; 52: Killiany RJ, Hyman BT, Gomez-Isla T, e al. MRI measures of enorhinal corex vs hippocampus in preclinical AD. Neurology 2002; 58: Frisoni GB, Laaso MP, Belmello A, e al. Hippocampal and enorhinal corex arophy in fronoemporal demenia and Alzheimer s disease. Neurology 1999; 52: Onnin AMH, Zwiers MP, Hoogman M, e al. Brain aleraions in adul ADHD: Effecs of gender, reamen and comorbid depression. Eur Neuropsychopharmacol 2014; 24: Perlov E, Philipsen A, Els L, e al. Hippocampus and amygdala morphology in aduls wih 695 aeniondefici hyperaciviy disorder. J Psychiary Neurosci 2008; 33: Milham Mair D, Mennes M, e al. The ADHD- 200 consorium: A model o advance he nslaional poenial of neuroimaging in clinical neuroscience. Fron Sys Neurosci 2012; 6:

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