Lung Cancer Detection Using CNN

##plugins.themes.bootstrap3.article.main##

Nagaraj Rathod

Abstract

Cancer is a quite common and dangerous disease. The various methods ofcancer exist in the worldwide. Lung cancer is the most typical variety of cancer.The beginning of treatment is started by diagnosing CT scan. The risk of death canbe minimized by detecting the cancer very early. The cancer is diagnosed bycomputed tomography machine to process further. In this paper, the lung nodules are differentiatedusingtheinputCTimages. Thelungcancernodulesareclassifiedusingsupportvectormachineclassifierandtheproposedmethodconvolutionalneuralnetworkclassifier.Trainingandpredictionsusingthoseclassifiers are done. The Nodules which are grown in the lung cancer are tested asnormal and tumor image. The testing of the CT images are done using SVM and CNN classifier. Deeplearningisalwaysgivenprominentplacefortheclassificationprocessinpresentyears. EspeciallythistypeoflearningisusedintheexecutionoftensorFlowandconvolutionalneuralnetworkmethodusingdifferentdeeplearninglibraries.

##plugins.themes.bootstrap3.article.details##

Section
Articles

References

[1]. Anita Chaudhary, Sonit Sukhraj Singh “Lung cancer detection on CT image susing image processing”, is computing sciences 2012 international conference,IEEE,2012.
[2]. G. Guo, S. Z. Li, and K. Chan, “Face recognition by support vector machines,”InProceedingsoftheIEEEInternationalConferenceonAutomaticFaceandGestureRecognition,Grenoble,France,pp.196-201,March2000.
[3]. D. Nurtiyasari, and R. Dedi, "The application of Wavelet Recurrent NeuralNetworkforlungcancerclassification,"ScienceandTechnology-Computer(ICST),2017 3rdInternational Conferenceon.IEEE,2017.
[4]. K. He, X. Zhang, S. Ren, and J. Sun, J. “Deepresidual learning for imagerecognition,”InProceedingsoftheIEEEconferenceoncomputervisionandpattern recognition(pp.770-778).2016.
[5]. E.Dandıl, M. Çakiroğlu, Z. Ekşi, M. Özkan, Ö. K.Kurt, andA. Canan,“Artificialneuralnetwork-basedclassificationsystemforlungnodulesoncomputed tomography scans,” in 6th International Conference of Soft Computingand PatternRecognition (soCPar),pp.382–386,IEEE,2014.
[6]. J. Cabrera, D. Abigaile and S. Geoffrey, "Lung cancer classification tool usingmicroarray data and support vector machines," Information, Intelligence, SystemsandApplications(IISA),20156th InternationalConferenceon.IEEE,2015.
[7]. H. M. Orozco and O. O. V. Villegas, “Lung nodule classification in CT thoraximages using support vector machines,” in 12th Mexican International Conferenceon ArtificialIntelligence,pp.277–283,IEEE,2013.
[8]. H. Krewer, B. Geiger, L. O. Hall et al., “Effect of texture features in computeraided diagnosis of pulmonary nodules in low dose computed tomography,” inProceedingsoftheIEEEInternationalnConferenceonSystems,Man,andCybernetics(SMC),2013,pp.3887–3891,IEEE,Manchester,UnitedKingdom,2013.
[9]. M. Abadi, P. Barham, J. Chen, Z. Chen, A.Davis, J. Dean, M.cDevin, S.Ghemawat, G. Irving, M. Isard, M. Kudlur, “TensorFlow: A System for Large-ScaleMachineLearning,”InOSDI 2016 Nov2(Vol.16,pp.265-283).
[10]. Arvind Kumar Tiwari, “Prediction Of Lung Cancer Using Image ProcessingTechniques”,AdvancedComputationalIntelligence:AnInternationalJournal(Acii),Vol.3,No.1, January2016.