Detection of Lung Cancer at Early Stage using Neural Network Techniques for Preventing Health Care

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1 IJSRD - Internatonal Journal for Scentfc Research & Development Vol. 3, Issue 4, 15 ISSN (onlne): Detecton of Lung Cancer at Early Stage usng Neural Network echnques for Preventng Health Care Megha Bhatnagar 1 Bhawana Malk Shash Bhushan yag 3 Prashant Naresh 4 1, Student 3,4 Assstant Professor 1,,3,4 Department of Computer Scence and Engneerng 1,3 UU, Utttarakhand, Inda,4 UPU, Lucknow, Inda Abstract Nowadays cancer has become huge threat n human lfe.here are dfferent knds of cancer, Lung cancer s common type of cancer causng very hgh ephemeralty rate. Lung cancer s a serous llness whch can be cured f t s dagnosed at pror stages. In ths paper, we address the problem of extracton and segmentaton the sputum cells based on the analyss of sputum color mage wth the am to attan a hgh specfcty rate and reduce the tme consumed to analyze such sputum samples. A lung cancer rsk predcton system s proposed here whch wll detect lung cancer at an early stage usng C scan mages of DICOM format. One of the key challenges s to remove whte Gaussan nose from the C scan mage, whch s done usng non local mean flter and to segment the lung Otsu s thresholdng s used. he textural and structural features are extracted from the processed mage to form feature vector. Image Processng plays sgnfcant role n cancer detecton, Varous technques used n Image Processng for nformaton retreval are Image acquston, Nose Removal, Segmentaton, and Morphologcal operatons etc. In ths approach, three classfers are appled for the detecton of lung cancer to fnd the severty of dsease. It has been found from results that SVM classfer acheves hgher accuracy of 95.1% whle ANN classfer acheves 9.68% accuracy on the gven data set and k-nn classfer shows least accuracy of 85.37%. Key words: Computer aded dagnoss, SVM, ANN, k-nn, C-Scan mages, Feature Extracton, Segmentaton I. INRODUCION Cancer has become a bg threat to humans globally among varous dseases, as per Indan populaton census data. Accordng to GLOBOCAN 1, an estmated 14.1 mllon new cancer cases and 8. mllon cancer-related deaths occurred n 1, compared wth 1.7 mllon and 7.6 mllon, respectvely, n 8. he most commonly dagnosed cancers worldwde were those of the lung (1.8 mllon, 13.% of the total) [1]. Lung cancer s consdered to be the man cause of cancer death, and n ts pror stages t s hard to detect because only n the advanced stage symptoms appear causng the ephemeralty rate to be the hghest among all other types of cancer []. If lung nodules can be recognsed accurately at a pror stage, the patent s survval rate can be ncreased by a sgnfcant percentage. Lung cancer s caused by uncontrollable rregular growth of cells n lung tssue. hese lung tssue abnormaltes are often called Lung nodules. hey are small and roughly sphercal masses of tssue, usually about 5 mllmeters to 3 mllmeters n sze. Researchers are becomng more and more concerned wth the elaboraton of automated CAD systems for lung cancer. Many publcatons proposed dfferent automated nodule recognton systems usng mage processng, and ncludng, dfferent technques for segmentaton, feature extracton and classfcaton. In the modern era of computerzed fully automated course of lvng, the feld of automated dagnostc systems plays a vtal role. Automated dagnostc system desgns n Medcal Image Processng s one such feld where numerous systems are proposed and stll many more under conceptual desgn due explosve growth of the technology today [3]. Data mnng tool has proved to be successful n dsease dagnoss [4]. Image processng s a method to convert an mage nto dgtal format and perform varous operatons on t to extract useful nformaton from t.. Extracton of certan features that characterze the nodule, but excludes the nsgnfcant attrbutes s the way of descrbng nodule. Image segmentaton s the process of assgnng a label to every pxel n an mage n such a way that pxels wth the same label share certan vsual characterstcs. After features are extracted, algorthms are used for classfyng the data nto the categores hs paper s organzed nto sx sectons. In secton related work carred out n ths feld s descrbed. In secton 3 proposed methodology for early detecton of cancer s explaned. In secton 4 system archtecture of nodule predctor s dscussed. In secton 5 expermental result and dscusson s explaned followed by concluson n secton 6. II. RELAED WORK In ths secton, some of the works on predcton of lung cancer, pre-processng, segmentaton and classfcaton technques have been dscussed: A two stage scheme for automatc lung nodules detecton n Mult-Slce Computed omography (MSC) [5] scans wth multple SVMs to reduce number of false postve wth accuracy of 87.8% s presented. An automatc CAD system [6] of 8% accuracy s developed for early detecton of lung nodule by analysng LUNG C mages usng several steps. Frst, several mage processng technques are appled to extract the lung regons from the C mage. After extracton, the extracted lung regons are segmented usng regon growng segmentaton algorthm. hen rule based technque s appled to classfy the cancer nodules. In the mage processng procedures of [7], processes such as mage pre-processng, segmentaton and feature extracton are dscussed n detal. An objectve and quanttatve assessment of centrosomal numeral and morphologcal abnormaltes n [8] s gven and the magntude of these dfferences s shown. Regons of nterest were selected to nclude one cell and ts centrosomes. After segmentaton, feature abstracton, and optmzaton, sx non All rghts reserved by 31

2 Detecton of Lung Cancer at Early Stage usng Neural Network echnques for Preventng Health Care redundant features were used for statstcal analyss and classfcaton. Optmal thresholdng [9] s appled to the denosed mage to segregate lung regons. Regon growng method s used to segment Lung nodules whch are of relatvely hgh densty found wthn the lung regons. An effcent lung nodule detecton scheme of accuracy 8.36% [1] s developed by performng nodule segmentaton through weghted fuzzy possblstc [11] based clusterng s carred out for lung cancer mages. An automatc Computer-Aded Detecton (CAD) scheme n [1] s presented havng accuracy 95% that can dentfy the pulmonary nodule at an early stage from C mages. A database s used to focus on denosng task n order to determne the benefts and drawbacks for each algorthm [13]. hs study present an overvew of dfferent algorthm for classfcaton and mage processng used n the feld of lung cancer predcton. Summary of varous segmentaton and classfcaton technques wth ther classfcaton accuracy and senstvty of nodule detecton has been presented, based on above s t has been found that not much work has been carred out for early detecton of lung cancer. Hence t has been taken up here. III. MEHODOLOGY Methodology s composed of two phases. 1) In frst phase, the C scan mages are pre-processed to remove Gaussan whte nose by usng non-local mean flter. As the accuracy of the segmentaton algorthm depends on the qualty of mages so, the mages are cleansed and segmented usng Otsu s thresholdng [14] and then, the textural and structural features are extracted from the segmented mage by the applcaton of feature extracton technques. ) In the second phase, the SVM classfer s mplemented and then t s traned and tested on sample data for the predcton of lung cancer. he output of the classfer s checked for accuracy whch s a measure of how accurately the classfer predcts the status of patent. nodule (tumor) by detectng boundary n mage usng canny edge detecton. hen, two largest regons are flled to remove extra muscle part from an mage except lungs. In nodule's feature extracton module, output of post processng s gven as nput to extract textural and structural feature of nodule after that SVM classfer s traned and tested on the bass of those features to provde fnal output.e severty of the dsease. he fgure.1 depcts the system archtecture of CAD system. A. Pre-Processng Pre-processng s the method to correct dfferent knd of errors n mages, done before processng. Pre-processng phase of the mages s necessary to mprove the qualty of the mages and make the feature extracton phase more relable and make t avalable for other phase. Preprocessng stage s mportant of a Computer Aded Dagnoss system for predcton of lung cancer les n ts ablty to remedy some of the problems that may occur due to some factors. B. Image Segmentaton he term mage segmentaton refers to the partton of an mage nto a set of regons.it refers to the process of parttonng the pre-processed C mage nto multple regons to separate the pxels or voxels correspondng to lung tssue from the surroundng anatomy. Image segmentaton s usually used to locate objects and boundares (lnes, curves, etc.) n mages.he goal n many tasks s for the regons that represent meanngful area of an mage. hresholdng, clusterng, comparson based, hstogram based, edge detecton and regon growng are several general-purpose technques have been developed for mage segmentaton. IV. SYSEM ARCHIECURE OF NODULE PREDICOR Lung Nodule predcton ams to automatcally predct the nformaton of nodule presented n lung s medcal mages by makng use of Computer Aded Dagnoss (CAD) system. he man dea of CAD s to assst radologsts n nterpretng medcal mages by usng dedcated computer systems to provde second opnons. CAD s a relatvely young nterdscplnary technology combnng elements of artfcal ntellgence and dgtal mage processng wth radologcal mage processng. CAD systems and technology show that CAD can help to mprove dagnostc accuracy of radologsts lghten the burden of ncreasng workload; reduce cancer mssed due to fatgue [15]. CAD system takes C scan mages of as nput and provdes status of patent as output on the bass of classfers. In lung C mage segmentaton process, Gaussan nose s removed from C scan mage whch s most common type of nose present n medcal mages. After that, segmentaton s done usng Otsu's thresholdng to segment the lung part n an mage. post processng enhancement s done to get clear mage for detecton of Fg.1 System Archtecture of CAD System All rghts reserved by 311

3 Detecton of Lung Cancer at Early Stage usng Neural Network echnques for Preventng Health Care C. Post Processng Post processng s done so that the lung mage wll become clearer n order to detect nodules.he post processng means fllng and thnnng. hnnng brngs down the wdth of the lne. Whle Fllng gets rd of small breaks and holes n the boundary, seperate extra part from mage and make mage more clear for nodule detecton. D. Feature Extracton Feature Extracton captures the man characterstcs of the nodule, and t s usually accepted that ths s one of the most challengng problems of nodule predcton.we can characterze the nodule by extracton of certan features, to defne the nodule,t excludes the nsgnfcant attrbutes. here are two man types of feature descrptors, namely textural features and structural features. 1) extural Feature: Computes the structural features value of nodule.e. Energy, Mean, and Standard Devaton. - ENERGY: s used to descrbe measure of nformaton n an mage, represented n equaton (.1). energy j Intensty (.1) - MEAN: he mean ntensty value ndcates the average ntensty value of all the pxels that belong to the same regon, calculated usng equaton (.). N 1 Meang Intensty N 1 (.) - SANDARD DEVIAION: s a measure of how much that gray levels dffer from mean, defned by equaton (.3). N 1 stdg Mean Intensty N 1 (.3) ) Structural Feature: Computes the structural features value of nodule.e. Area, Convex Hull Area, Equv Dameter and Soldty. - AREA: It s a scalar value that gves the actual number of pxels n the Regon Of Interest (ROI). - CONVEX AREA: Scalar value that gves the number of pxels n convex mage of the Regon Of Interest whch s a bnary mage wth all pxels wthn the hull flled n. - EQUIV DIAMEER: It s the dameter of a crcle wth the same area as the Regon Of Interest, defned n (.4). Equv dameter 4*area (.4) - SOLIDIY: It s the proporton of the pxels n the convex hull that are also n the Regon Of Interest as defned n (.5). area soldty convexarea (.5) he extracted features are then used by the classfer ether for tranng the system or for classfyng the nodule. E. Classfcaton After features are extracted, algorthms are used for classfyng the data nto the group. hese are also known as classfers. here are two knds of classfcaton: supervsed classfcaton and unsupervsed classfcaton. If the feature vectors are gven wth known labels (the correspondng correct outputs), then the tranng s called to be supervsed. If the classfer categorzes the data automatcally wthout any use of class labels, then t s known to be unsupervsed classfcaton. Supervsed classfers are Support Vector Machne (SVM), k-nearest Neghbors (k-nn), Artfcal Neural Networks (ANN), and Classfcaton rees. Unsupervsed classfers are k-means clusterng, mxture models, herarchcal clusterng, Prncpal Component Analyss, Sngular Value Decomposton etc [16]. SVM, ANN and k-nn are three supervsed classfcaton technques dscussed here. 1) SVM Classfer: SVMs are a set of related supervsed learnng methods used for classfcaton and regresson. an SVM wll construct a separatng hyperplane for vewng nput data as two sets of vectors n an n-dmensonal space,we check the one whch maxmzes the margn between the two data sets. hen construct two parallel hyper-planes for calculatng margn, Good separaton s acheved by the hyper-plane to calculate the largest dstance to the neghborng data ponts of both classes, snce n general the larger the margn the lower the generalzaton error of the classfer. he MMH can be rewrtten as the decson boundary[17] as descrbed n equaton (.6). d Where, l X 1 y X X b y s the class label of support vector X. (.6) X s a test tuple. and b are numercal permeters they were determned automatcally by the optmzaton or SVM algorthm. l s the no. of support vector. Kernel functon s used to make (mplct) nonlnear feature map e.g. Polynomal kernel, Radal bass functon (RBF) kernel. ) RBF Kernel: he RBF kernel as n equaton (.7) s one of the most popular kernel functons. It adds a bump around each data pont. he RBF kernel s a measure of smlarty between two examples. he feature space s nfnte-dmensonal. K( x; x') exp( x x' (.7) 3) ANN Classfer: ANN usually called "neural network",s a mathematcal model or computatonal model based on bologcal neural networks. It conssts of an nterconnected group of artfcal neurons and processes nformaton usng a connectonst approach to computaton. In most cases an ANN s an adaptve system that changes ts structure based on external or nternal nformaton that flows through the network durng the learnng phase. Neural networks are non-lnear statstcal data modelng tools. It can be modelled to defne complex relatonshp. ) All rghts reserved by 31

4 4) K-NN Classfer: he k-nearest neghbors algorthm (k-nn) s a method for classfyng objects that s based on closest tranng examples n the feature space. Instance-based learnng s same as K- NN, or lazy learnng where the functon s only approxmated locally and all computaton s deferred untl classfcaton. Detecton of Lung Cancer at Early Stage usng Neural Network echnques for Preventng Health Care V. EXPERIMENAL RESULS AND DISCUSSION he varous Performance Metrcs (Accuracy, Precson, Recall, and Specfcty) for the test data are shown n able1. he formulatons are shown n percentage, each column ndcates the neural networks Classfer used and the rows ndcate the Metrc value respectvely. Classfer SVM ANN KNN Accuracy(%) Precson(%) Recall(%) Metrcs Specfcty(%) able 1: Performance Metrcs In Percentage For est Data From table I t s shown that accuracy of SVM s 95.1% whch s better that ANN classfer (9.68%) and k- NN classfer (85.37%). SVM predcts mages of stage I and stage II more accurately. he graph of the able I s gven n Fgure Accuracy Precson Recall Specfcty SVM ANN k-nn Fg. : Performance metrcs of Classfers hese results shown n fgure wth 4 mages of stage I and 17 mages of stage II s used as test dataset. Value of accuracy, precson, recall and specfcty s n percentage (%). For expermentaton of the technque, the C mages are obtaned from a NIH/NCI Lung Image Database Consortum (LIDC) dataset. hese mages are progressed to ths system. he dagnoss rules are then produced from those mages and these rules are progressed to the classfer for the learnng procedure. After learnng operaton a lung mage s progressed to the proposed system. hen the proposed system executes ts processng steps and fnally t wll detect whether the suppled lung mage s wth cancer or not. he presented CAD system s capable of detectng lung nodules wth dameter.45 mm, whch means that the system s capable of detectng lung nodules when they are n ther ntal stages. hus early dagnoss of cancer wll mprove the patent s survval rate. Fg. 3: Segmentaton steps: (a) Orgnal, (b) Pre-processng (c) hresholdng (d) Segmentng Lung Regon, (e) Showng Cancerous Nodule VI. CONCLUSION he feld of Dsease Dagnoss s a contnuously evolvng and very actve feld for research. he purpose of the current approach s to predct the status of patent for ntal stage detecton of lung cancer. A novel approach for predctng Lung cancer nodule at pror stage usng SVM Classfer has been presented here. he Structural and extural Features have been used for reportng the nodule. he results got are very stmulatng, data was tested on Support Vector Machne Classfer wth RBF kernel obtaned an accuracy of 95.1%. he classfcaton rates obtaned for the SVM, ANN and k-nn Classfer were 95.1%, 9.68% and 85.37% respectvely. ACKNOWLEDGMEN Intally, we would lke to thank our almghty n the success of completng ths work. We would extend our grattude to all the experts for ther crtcal comments. REFERENCES [1] Ferlay J, Soerjomataram I, Ervk M, Dksht R, Eser S, Mathers C, Rebelo M, Parkn DM, Forman D, Bray, F (13). GLOBOCAN 1 v1., Cancer Incdence and Mortalty Worldwde: IARC CancerBase No. 11, Lyon, France: Internatonal Agency for Research on Cancer. [] S.Shak Parveen, C.Kavtha, "Detecton of lung cancer nodules usng automatc regon growng method", Proceedngs of the 4th Internatonal Conference on Computng, Communcatons and Networkng echnologes (ICCCN), 13, pp. 1-6 [3] Guruprasad Bhat, Vdyadev G Bradar, H Sarojadev Naln,"ArtfcalNeural Network based Cancer Cell Classfcaton (ANN C3)", Computer Engneerng and Intellgent Systems, vol. 3, (), 1, pp [4] Ada, Rajneet Kaur, "A Study of Detecton of Lung Cancer Usng DataMnng Classfcaton echnques", Internatonal Journal of Advanced Research n Computer Scence and Software Engneerng, vol. 3, (3), 13, pp All rghts reserved by 313

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