DETECTION AND CLASSIFICATION OF BRAIN TUMOR USING ML

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1 DOI: Volume 9, No. 2, March-Aprl 208 Internatonal Journal of Advanced Research n Computer Scence RESEARCH PAPER Avalable Onlne at ISSN No DETECTION AND CLASSIFICATION OF BRAIN TUMOR USING ML Avrup Chowdhury Department of Informaton Technology Meghnad Saha Insttute of Technology Kolkata, Inda Indrat Das Department of Informaton Technology Meghnad Saha Insttute of Technology Kolkata, Inda Avpsa Roy Chowdhury Department of Informaton Technology Meghnad Saha Insttute of Technology Kolkata, Inda Arnab Halder IBM Inda Pvt. Ltd. Kolkata, Inda Abstract: Bran tumor detecton and classfcaton s the most dffcult and tedous task n the area of medcnal mage preparng. (Magnetc Resonance Imagng) s a medcnal procedure, generally adopted by the radologst for representaton of nner structure of the human body wth no surgery. gves abundant data about the human delcate tssue, whch helps n the concluson of bran tumor. Precse segmentaton of mage s basc for the concluson of bran tumor by computer supported clncal devce. Ths paper s focused towards the desgn of an optmal and more accurate way for the detecton of tumor from bran scans and f t confrms the presence of tumor then t s focused on evaluatng ts stage,.e., bengn or malgnant. We have expermentally shown that our proposed methodology has a greater accuracy than other exstent methods for classfyng tumor type to be ether as Malgnant or Bengn snce the maxmum accuracy for detecton of malgnant tumor s 99.02% and for Bengn tumor s 99.67%. Keywords: malgnant tumor; bengn tumor; ansotropc dffuson flter; support vector machne; morphologcal operaton. INTRODUCTION The human bran s the senstve organ of the body whch controls the other part of the body. Ths correspondence s done wth the assstance of neural framework. Each secton of the bran has some partcular work that coordnates human movements. However, when a porton of the bran develops to an unnatural sze then the work done by the bran get hampered and some of the tme brans may stop ts ordnary behavor. Ths unusual enlargement of the bran cells s named as 'bran tumor' n medcal scence. A tumor can be characterzed as a group of unusual cells ncreasng nsde the bran. The correct explanatons for bran tumors are stll at the darkest sde of medcal scence yet the genune mpacts of bran tumors are watched, at tmes t shows unusual human actvtes, nternal cavty paralyss, and few cases may become a threat to human lfe []. Thus, to battle ths ssue, an exact dagnoss s exceptonally deal. In the last few decades, we have encountered a couple of cuttng-edge technques, among whch computer-based magng s the most favored one, n the determnaton of bran tumors that are valued and acknowledged n surgcal plannng and further treatment. In neuroscence and, the bran s broadly acknowledged magng strategy. The s the most regularly utlzed methodology for magng bran tumors and recognton of ts terrtory. The customary strategy for CT and bran mages groupng and tumor recognton are stll for the most part n lght of an mmedate human nvestgaton of those mages, n spte ther beng varous other dverse technques have ust been proposed[2,3]. s a non-destructve and non-nvasve strategy n nature. It gves hgh-resoluton mages whch are generally utlzed as a part of bran scannng reason. There are many mage processng method, for example, hstogram equalzaton, pcture segmentaton, mage enhancement, morphologcal operaton, feature choce and obtanng the features, and order. The remanng sectons of the paper are as follows. Secton II, dscusses about the revew of some exstng research work towards the detecton of bran tumor and ts classfcaton. Secton III, descrbes our own proposed method whch s adopted. In secton IV, the expermental results are shown n a tabular form whch we have obtaned for detecton. And last secton descrbe the concluson. 2. LITERATURE SURVEY Over the decades, dfferent specalsts have worked n the space of bran tumor detecton and groupng and they have used and formulated a stack count that surveys the mplementaton of ther proposed methodologes and plans. In ths secton, we have propelled a few summares of such exstng studes and technques. Researchers A.S. Swakshar et al. [4] have suggested a strategy that accomplshes tumor stage by utlzng ANN. In the pre-processng stage, three dstnctve dfferentaton upgrade plans have been connected; I) adusted ) adaptve threshold and ) hstogram magng. The TKFCM calculaton whch s bascally a combned approach of the K-mples and Fuzzy C-mples plans has been embraced wth specfc alteratons for actualzng the dvson organze. In the feature extracton the property based measurement features have been nferred. At long last, the SVM conspre characterzes the bran pcture ether nto the normal or havng tumor classes. The Bran Tumor 205-9, IJARCS All Rghts Reserved 585

2 Avrup Chowdhury et al, Internatonal Journal of Advanced Research n Computer Scence, 9 (2), March-Aprl 208, arrange s ordered utlzng the ANN classfer. The dataset for every mage of the normal bran, malgnant tumor, and the amable tumor has been removed from 39 pctures out of whch 3 normal, 9 bengn, 7 malgnant I, 6 malgnant II, 3 malgnant II, and malgnant IV organze tumor bran mages have been effectvely dstngushed. The precson of the proposed strategy was expected around 97.44%. Researchers G. Sngh et al. [5] have contrved a novel strategy for bran tumor dentfcaton that envelops Hstogram Normalzaton and selecton of K-mples/ K- means Segmentaton schemes. In ths present work under scrutny at, to begn wth, the nput mage s pre-prepared to de-commoton undesrable sgns from examnes utlzng shfted channels lke Medan channel, Adaptve channel, Averagng channel, Un-sharp coverng channel and Gaussan channels. The hstogram of the pre-prepared mage s then standardzed and arrangement of examne s encouraged. In the end, the pcture s sectoned by recevng the K-means calculaton to solate the tumor from the output. s can be productvely grouped the SVM n order to offer exact expectaton and characterzaton. SVM classfer allegedly gave the precson of 9.49%. As obvously apparent, the SVM approach offered hgher precson. Researchers H.B.Nandpuru et al. [6] have receved a scholarly groupng framework to sort normal and abnormal bran examnes where the scan experences three stages n partcular; I) mage pre-processng, ) features or hghlght extracton and ensung ) classfcaton. Amd the pre-preprocessng stage, the RGB parts of the bran are changed nto grey scale mage. Next, the Medan Flter has connected to de-clamor the checks. At last, Skull Maskng approach s utlzed to solate non-mnd tssues from bran mages. Enlargement and Eroson are two noteworthy morphologcal errands that are used for realzng the skull coverng the technque. In the second perod of feature extracton, the surface features of the scan lke symmetrcal, grayscale portons are removed. Fnally, n the classfcaton step, dverse machne learnng strateges lke SVM, KNN and SVM-KNN have been embraced and a smlar report among them s encouraged. The dataset contaned 50 pctures and t was nferred that the Hybrd classfer SVM-KNN conspre offered the most elevated precson rate of 98% when contrasted wth ts partners. Researchers Parveen et al. [7] have come up wth an algorthm that s a mx of SVM and fuzzy c-mples, a hybrd scheme for recognton of bran tumor from scans. Here the pcture qualty s enhanced utlzng the complexty change and md-range stretch procedures. In addton, morphologcal actvtes lke the Double thresholdng plan have been receved for skull strpng. Wth the end goal of mage segmentaton and feature extracton, FCM bunchng and GLRLM s actualzed ndvdually. The nformatonal ndex comprsed of 20 bran outputs of patents; out of whch 96 was embraced to prepare the SVM classfer and the rest of the 24 checks were used to test the prepared SVM. SVM classfer workng under the Lnear, Quadratc and Polynomal kernel functon modes detaled precson level of 9.66%, 83.33% and 87.50% ndvdually and was guaranteed to offer 00% accuracy. Researchers T.C. Sarma et al. [8] uses hstogram, whch computes the total quantty of specfed pxel values dstrbuted n a partcular mage. Fnally the Classfcaton and dentfcaton stages are facltated usng k-nn whch s based on tranng of k value. Interestngly n ths work the Manhattan metrc has also been ncorporated to estmate the dstance of the classfer. The algorthm was tested on 48 mages where the overall accuracy rates for all mages were around 95%. In the next secton, we have proposed our methodology to detect and classfy bran tumor from bran MR mages that we have deduced by overcomng the found lmtatons on the subect. 3. PROPOSED METHODOLOGY Bran tumor s always consdered as one of the most dangerous and lfe threatenng dsease for the patents and fatal as well. The earlest and accurate detecton of such knd of tumors can only provde the correct dagnoss whch can lead to medcal healng of the patent. Here, n ths paper, we have descrbed our obectve n two parts, the frst half deals wth detecton of bran tumor.e. the presence of the tumor n the provded. The other part,.e. the second part contans the classfcaton of the tumor. Bascally, here we wll analyze the mages whch wll conclude the stage of the tumor as bengn or malgnant. In general, the block dagram for our process,.e. mage segmentaton and classfcaton s depcted n Fg..The nput mages wll undergo varous stages whch can be summarzed as follows, Image Acquston, Flterng, feature extracton and classfcaton of mages. A. Detecton of Bran Tumor The proposed model s capable to detect the bran tumor through morphologcal operatons on nput mages. To pave the way for morphologcal operaton on mage, the mage was frst fltered usng Ansotropc Dffuson Flter [9] whch reduces the contrast between adacent pxels of the workng mage. Then, usng a threshold pxel value the whole mage s converted nto a greyscale one programmatcally. Ths ntal flter s qute effcent n detectng the exact poston of the tumor, f present. On ths sem-processed mage next morphologcal operatons are appled and nformaton of soldty and probable tumor locatons are obtaned. A mnmum value of both the above mentoned crtera s hence determned from a statstcal average of dfferent mages whch contan tumors. Thus a fnal detecton result s obtaned and produced further. Ansotropc Dffuson Flter [9] Ansotropc dffuson flter, proposed by Persona and Malk, s a strategy for expellng nose from nput pctures. Ths strategy s utlzed for smoothng the pcture by savng requred edges and structures. The essental thought s smply to modfy the smoothng level n a regon based on the edge structure n the area. Homogenous portons are hghly smoothed and sold edge areas are scarcely smoothed (to save the structure). Morphologcal Operatons [0] An mage s a set of pxels and morphologcal operatons are done on those mage pxels. Bnary morphology utlzes ust set membershp and doesn't manage the parameters, for 205-9, IJARCS All Rghts Reserved 586

3 Avrup Chowdhury et al, Internatonal Journal of Advanced Research n Computer Scence, 9 (2), March-Aprl 208, example, grey level or colour value of a pxel. Ths process s dependent on the orderng of pxels of the mage and on several occason s appled to bnary or gray scale mages. Bnary mages can be changed to the clent's partculars by ntroducng processes lke eroson, dlaton, openng and closng. As a matter of fact bnary pctures or hghly contrastng pctures can have ust two knds of pxel shadng esteems. Numercally, those two shadng esteems are regularly 0 for black, and ether or 255 for whte. Ths knd of bnary mages are obtaned after processng a gray scale or may be color mages n order to solate the requred obect n the mage from the background. The color of the obect (usually whte) denotes the foreground color and the rest (usually black) refers to the background color. Confrmaton of tumor based on Morphologcal Operator When the mage s converted nto a bnary formatted one, varous morphologcal operatons are then appled on top of the mage. The obectve of the morphologcal operators s to separate out the tumor part of the mage from the mage tself. The part of the tumor n the mage s clearly vsble as whte colour, whch s used to denote the affected tumor zone n the mage. It has the utmost ntensty among all color values used n dfferent parts of the mage. snusodal components. The Dscrete Wavelet Transformaton (DWT) s qute smlar to the DFT. Both DFT and DWT express the orgnal sgnal as a combnaton of smpler sgnal called basc functon. DCT and DFT use snusodal waves as basc functons whereas Wavelet Transform use small waves of varyng frequency and of lmted extent as bass functon. Ths s known as wavelets. DWT can analyze the sgnal at dfferent resoluton. It deals wth an approxmated coeffcent and detal coeffcent [2, 3]. Ths resembles passng the sgnal through several bandpass flters. Successve low-pass and hgh-pass flterng of the sgnal and down samplng the sgnal after each flters s beng done. DWT can be executed n multple levels. The data matrx used n each level s the approxmaton matrx generated n the prevous level. In 2D wavelet decomposton, the wavelet transforms can be appled agan on the low pass - low pass (LL) verson of the mage, yeldng seven sub mages. Hence N level decomposton n 2D cases resultng n 3N+ dfferent frequency bands namely, LL, LH, HL and HH. Classfcaton usng Support Vector Machne Support vector machnes are a supervsed machne learnng algorthm whch s used for analyzng hgh-dmensonal data. SVM were frst proposed by Vapnk. It has the capacty of learnng non-lnear appropraton of the genune nformaton wthout utlzng any earler nformaton [4]. As ndcated by Statstcal Learnng Theory, the arrangement of the deal order level wth most promnent characterzaton edge can delver an deal productvty of SVM [5]. One-class SVM sets up a classfer ust from an accumulaton of marked postve formats called "postve tranng tests" [6]. Assumng that the clent has the sequent tranng set X = {x, where =, 2, 3... l} and l N s the amount of dscernment. Assume that tranng nformaton s mapped nto feature space F,.e. Fgure. Proposed Method φ:x F () Tranng sample X φ(x ) n F. If there s a functon ƒ whch takes the amount + for tumor and for non-tumor, after that n F, the data can be dvded from the source wth the maxmal margn. So ust the tumor nformaton s deemed and the obect functon s detaled as: B. Tumor Classfcaton: Bengn or Malgnant The process of classfcaton of bran tumor starts wth feature extracton of the mage. Several feature extracton algorthms exst but Wavelet Transform Decomposton technque s used for ths purpose. Fnally Support Vector Machne (SVM) s appled to classfy the tumor whether that s bengn or malgnant n nature. Feature extracton usng DWT So far we are done wth the pre-processng stage of the nput mage, and now the pre-processed mage wll undergo a dscrete wavelet transform decomposton technque. Now the mportant features are extracted from the decomposed mage. Then the extracted features are combned and normalzed. The Dscrete Fourer Transform (DFT) s a mathematcal transform operaton [] whch s used to convert dgtal sgnal from the spatal or temporal doman to the frequency doman. Then the frequency doman sgnal s expressed as a set of coeffcents whch s a factor of known T t mn W W +,,, η b W F η R b R 2 vl s. t. W. φ( x ) b η, η 0. (2) W = the typcal vector of hyperplane whch depct the decson lmt. b= depcts the threshold of functonƒ. ηі= the slack varable whch s condemned n the target functon. ν = regularzaton term, a clent characterzed parameter whch controls the trade off and demonstrates the fracton of samples that ought to be acknowledged by the depcton. Approprate W and b are to be found to lmt (2). Here for every one of the dsparty compels n eq. (2), the postve Lagrange coeffcents, αі and βі (for =, 2, 3,..,.l), was presented. Ths gves the accompanyng Lagrange frame 205-9, IJARCS All Rghts Reserved 587

4 Avrup Chowdhury et al, Internatonal Journal of Advanced Research n Computer Scence, 9 (2), March-Aprl 208, L( W, η, b, α, β ) = W 2 β η W + vl α ( W. φ( x ) b + η ) T η b. (3) where η, α and β are one-column vectors dsplayng [ηі], [αі] and [βі], respectvely. To mnmze eq. (3), let ts gradent, wth respect to W, band і, ndvdually, be equal to zero, that s l = W α φ x ) = 0 W = η l b And α φ( x ) ( = + α = 0 α = l = + α β = 0 α = β η Replacng (4)-(6) nto (3), we get mn 2, α α k( x, x ) s. t. 0 α, α = k( x, x ) = φ( x ). φ( x ).(4) (5)... (6).. (7) Equaton (7) can be further wrtten n a more compressed matrx form T mn α Qα 2, T s. t. 0 α, e α = φ, = k( x, x ).. (8) e = a measure vector of length N. The double ssue n (8) demonstrates a notable quadratc frame and ts mnmzaton can be understood by utlzng the notable quadratc programmng (QP) mprovement technque. The deal amount α relates to the base of the target functon. Those queston wth weght α >0 s requred n the last artculaton of the nformatonal ndex. They are ordnarly called support vectors n machne learnng research. The deal measure of b can be fgured by equaton (9). Where x = any one of the support vectors. The tumor part can be classfed, when the optmal values of the parameters are obtaned, accordng to the followng decson functon f ( x) = sgn( α k( x, x) b).. (0) The data comparng to ƒ(x) 0 are resolved as tumor data applcants. If not, they are vewed as non-tumor zones. The learnng capacty of one-class SVM exudes from the kernel trck [7]. Ths trck s performed by dfferent choce of k(x,y) ntroduced n (7). Notce that n the defnton of oneclass SVM, the mappng φ s ust relegated verfably by part k(x,y). An adequate bt ought to be depcted,.e., an approprate bt can delneate target nformaton nto a lmted crcularly formed terrtory n the element space and blueprnt the tems outsde the nformaton lmt. Wth the "kernel trck", one-class SVM can manage nonlnear multmode data dsperson [7]. 4. EXPERIMENTAL RESULT The experments were carred out on the platform of AMD A8 wth 2 GHz processor and 8 GB RAM, runnng under Wndows 8. operatng system. The algorthm was n-house developed va the wavelet toolbox, of Matlab 207b. We downloaded the open SVM toolbox and appled t to the MR bran mages classfcaton. The programs can be run or tested on any computer platforms where Matlab s avalable. Our experment was carred out n two noteworthy parts, frst dealng wth the detecton of bran tumor, that f present proceeds to the second part whch s, fndng the type of tumor present,.e., Malgnant or Bengn. Fg 2 and Fg 3 shows the frst part that s the detecton of tumor n the bran scan, whether tumor s present or not present. Fg 3.a s the nput scan, then flterng s done usng ansotropc flter whch s shown n Fg 3.b. After flterng, morphologcal operatons are performed to detect the tumor as shown n Fg 3.c, and Fg 3.d confrms the presence of the tumor. Further the tumor boundary s detected n Fg 3.e. And fnally n Fg 3.f the detected tumor s marked wth a red boundary n the bran. Fgure 2. No Tumor n b = k( x, x ) α. (9) 205-9, IJARCS All Rghts Reserved 588

5 Avrup Chowdhury et al, Internatonal Journal of Advanced Research n Computer Scence, 9 (2), March-Aprl 208, Fgure 3. a. Input Image b. Flter Image c. Depctng Boundary d. Presence of Tumor e. Tumor Boundary f. Detected Tumor Now the second part of our experment shows f the tumor s present, then t s of whch type, Malgnant or Bengn. Fg 4 and Fg 5 shows the detecton of Bengn and Malgnant speces of tumor respectvely. Fgure 4. Bengn Tumor Detecton Fgure 5. Malgnant Tumor Detecton In Table, the feature parameters lke Entropy, RMS, Smoothness, Skewness, IDM, Correlaton, Energy, Homogenety, etc. that we have used to classfy the tumor nto Malgnant and Bengn for 0 bran, out of whch 5 were Malgnant and 5 were Bengn, have been checked. Addtonally the correspondng values for the factors have been noted for the result. After that we have done an extensve comparatve analyss wth dfferent classfcaton algorthms wth our proposed algorthm. From Fg. 6 and Fg. 7, we can see that for detecton of malgnant and bengn tumor, the proposed algorthm performs much better than other exstng algorthm. For Malgnant tumor detecton, the mnmum accuracy for proposed algorthm s 97.22% and maxmum accuracy s found to be 99.02%, whereas for lnear kernel, RBF kernel and Polynomal kernel classfcaton shows a maxmum accuracy of 89%, 84% and 77% respectvely. And for Bengn tumor detecton, the mnmum accuracy for proposed algorthm s 96.72% and maxmum accuracy s found to be 99.67%, whereas lnear kernel, RBF kernel and Polynomal kernel classfcaton shows a maxmum accuracy of 92.56%, 84.27% and 8.9% respectvely. So, we can confrm that our classfcaton technque has a greater accuracy than any of the exstent algorthms. Table I. Lst of Features for Detecton of Malgnant and Bengn Tumor Images Mean Standard Devaton Entropy RMS Varance Smoothness Kurtoss Skewness IDM Contrast Correlaton Energy Homogenety Tumor Type Image Malgnant Image Malgnant Image Malgnant Image Malgnant 205-9, IJARCS All Rghts Reserved 589

6 Avrup Chowdhury et al, Internatonal Journal of Advanced Research n Computer Scence, 9 (2), March-Aprl 208, Image Malgnant Image Bengn Image Bengn Image Bengn Image Bengn Image Bengn procedure conssts of four stages namely: ansotropc flterng, morphologcal operatons, feature extracton and classfcaton. The proposed model s capable of detectng tumor by conductng morphologcal operatons on nput mages by employng the mage flterng scheme usng Ansotropc Dffuson Flter. Wavelet Transform Decomposton technque s used for feature extracton purpose. Fnally Support Vector Machne (SVM) s appled to classfy the tumor whether that s bengn or malgnant n nature. From the conducted experments t can be concluded that for detecton of malgnant tumor the accuracy rate s 99.02% whereas for bengn tumor t s 99.67% accurate whch s sgnfcantly hgher than the exstent face detecton algorthms pertanng to ths doman. REFERENCES Fg 6. Comparatve Analyss of Accuracy for Malgnant Tumor Detecton Fg. 7. Comparatve Analyss of Accuracy for Bengn Tumor Detecton 5. CONCLUSION In ths paper, an optmal way for the detecton of tumor from bran scan has been devsed whch on successful detecton classfes the type: bengn or malgnant. The entre [] J. Selvakumar, A. Lakshm, T. Arvol, Bran Tumor segmentaton and ts area calculaton n bran MR mages usng K mean clusterng and Fuzzy C - mean algorthm, Internatonal Conference on Advances n Engneerng, Scence and Management (ICAESM), 202, pp , ISBN: [2] A. Islam, S.M.S Reza, K.M Iftekharuddn, Multfractal Texture Estmaton for Detecton and Segmentaton of Bran Tumors, IEEE Transactons on Bomedcal Engneerng, Vol. 60, Issue, 203, pp , ISSN: [3] S. Bauer, C May, D Donysou, G. Stamatakos, P. Buchler, M. Reyes, Multscale Modelng for Image Analyss of Bran Tumor Studes, IEEE Transactons on Bomedcal Engneerng, Vol. 59, Issue, 202, pp , ISSN: [4] R. Ahmmed, A.S. Swakshar, Md. F.Hossan, Md.A. Rafq, Classfcaton of Tumors and It Stages n Bran Usng Support Vector Machne and Artfcal Neural Network, Internatonal Conference on Electrcal, Computer and Communcaton Engneerng (ECCE), 207, pp , ISBN: [5] G. Sngh, M.A. Ansar, Effcent Detecton of Bran Tumor from s Usng K-Means Segmentaton and Normalzed Hstogram, st Inda Internatonal Conference on Informaton Processng (IICIP), 206, pp. -6, ISBN: , IJARCS All Rghts Reserved 590

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