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An intelligent lung tumor diagnosis system using whale optimization algorithm and support vector machine

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Abstract

Medical image processing technique are widely used for detection of tumor to increase the survival rate of patients. The development of computer-aided diagnosis system shows improvement in observing the medical image and determining the treatment stages. The earlier detection of tumor reduces the mortality of lung cancer by increasing the probability of successful treatment. In this paper, the intelligent lung tumor diagnosis system is developed using various image processing technique. The simulated steps involve image enhancement, image segmentation, post-processing, feature extraction, feature selection and classification using support vector machine (SVM) kernel. Gray level co-occurrence matrix method is used for extracting the 19 texture and statistical features of lung computed tomography (CT) image. Whale optimization algorithm (WOA) is considered for selection of best prominent feature subset. The contribution provided in this paper is the development of WOA_SVM to automate the aided diagnosis system for determining whether the lung CT image is normal or abnormal. An improved technique is developed using whale optimization algorithm for optimal feature selection to obtain accurate results and constructing the robust model. The performance of proposed methodology is evaluated using accuracy, sensitivity and specificity and obtained as 95%, 100% and 92% using radial bias function support vector kernel.

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References

  • Abdillah B, Bustamam A, Sarwinda D (2017) Image processing based detection of lung cancer on CT scan images. J Phys Conf Ser 893(1):012063

    Article  Google Scholar 

  • Ada RK (2013) Early detection and prediction of lung cancer survival using neural network classifier

  • Al-Tarawneh MS (2012) Lung cancer detection using image processing techniques. Leonardo Electron J Pract Technol 11(21):147–158

    Google Scholar 

  • Armato SG III, Sensakovic WF (2004) Automated lung segmentation for thoracic CT: impact on computer-aided diagnosis1. Acad Radiol 11(9):1011–1021

    Article  Google Scholar 

  • Armato SG, Giger ML, MacMahon H (2001) Automated detection of lung nodules in CT scans: preliminary results. Med Phys 28(8):1552–1561

    Article  Google Scholar 

  • Asuntha A, Brindha A, Indirani S, Srinivasan A (2016) Lung cancer detection using SVM algorithm and optimization techniques. J Chem Pharm Sci (JCPS) 9(4):3198–3203

    Google Scholar 

  • Deshpande AS, Lokhande DD, Mundhe RP, Ghatole JM (2015) Lung cancer detection with fusion of CT and MRI images using image processing. Int J Adv Res Comput Eng Technol (IJARCET) 4(3):763–767

    Google Scholar 

  • Farag A, Graham J, Farag A (2010) Robust segmentation of lung tissue in chest CT scanning. In: 2010 17th IEEE international conference on image processing (ICIP). IEEE, pp 2249–2252

  • Gaikwad A, Inamdar A, Behera V (2016) Lung cancer detection using digital image processing On CT scan images. Int Res J Eng Technol (IRJET) e-ISSN, 2395-0056

  • Gajdhane AV, Deshpande LM (2014) Detection of lung cancer stages on CT scan images by using various image processing techniques. IOSR J Comput Eng (IOSR-JCE) 16(5):28–35

    Article  Google Scholar 

  • George RJ, Kumari DAJ (2014) Segmentation and analysis of lung cancer image using optimization technique. Int J Eng Innov Technol (IJEIT) 3(10):191–195

    Google Scholar 

  • Gomathi M (2012) An effective classification of benign and malignant nodules using support vector machine. J Glob Res Comput Sci 3(7):6–9

    Google Scholar 

  • Gomathi M, Thangaraj P (2010) A computer aided diagnosis system for lung cancer detection using support vector machine. Am J Appl Sci 7(12):1532

    Article  Google Scholar 

  • Gurcan MN, Sahiner B, Petrick N, Chan HP, Kazerooni EA, Cascade PN, Hadjiiski L (2002) Lung nodule detection on thoracic computed tomography images: preliminary evaluation of a computer-aided diagnosis system. Med Phys 29(11):2552–2558

    Article  Google Scholar 

  • Indira Priyadharsini S, Mangayarkarasi N, SaiRamesh L, Raghuraman G (2018) Lung nodule detection on CT images using image processing techniques. Int J Pure Appl Math 119(7):479–487

    Google Scholar 

  • Joon P, Bajaj SB, Jatain A (2019) Segmentation and detection of lung cancer using image processing and clustering techniques. In: Pati B, Panigrahi CR, Misra S, Pujari AK, Bakshi S (eds) Progress in advanced computing and intelligent engineering. Springer, Singapore, pp 13–23

    Chapter  Google Scholar 

  • Kanitkar SS, Thombare ND, Lokhande SS (2015) Detection of lung cancer using marker-controlled watershed transform. In 2015 international conference on pervasive computing (ICPC). IEEE, pp 1–6

  • Katiyar P, Singh K (2017) Lung tumor detection and segmentation in CT images

  • Kaur T, Gupta EN (2015) Classification of lung diseases using optimization techniques. Int J Sci Res Dev 3(8):852–854

    Google Scholar 

  • Kavitha P, Ayyappan G (2018) Lung cancer detection at early stage by using SVM classifier techniques. Int J Pure Appl Math 119(12):3171–3180

    Google Scholar 

  • Keziah T, Haseena P (2018) Lung cancer detection using SVM classifier and MFPCM segmentation. Int Res J Eng Technol (IRJET) 5(4):3114–3118

    Google Scholar 

  • Kohad R, Ahire V (2014) Diagnosis of lung cancer using support vector machine with ant colony optimization technique. Int J Adv Comput Sci Technol (IJACST) 3(11):19–25

    Google Scholar 

  • Kohad R, Ahire V (2015) Application of machine learning techniques for the diagnosis of lung cancer with ANT colony optimization. Int J Comput Appl 113(18):34–41

    Google Scholar 

  • Kumari I, Sharma P (2015) Lung cancer segmentation and prediction techniques review. Int J Adv Eng Glob Technol 3(11):1374–1379

    Google Scholar 

  • Kuruvilla J, Gunavathi K (2014) Lung cancer classification using neural networks for CT images. Comput Methods Programs Biomed 113(1):202–209

    Article  Google Scholar 

  • Lee SU, Chung SY, Park RH (1990) A comparative performance study of several global thresholding techniques for segmentation. Comput Vis Graph Image Process 52(2):171–190

    Article  Google Scholar 

  • Lemjabbar-Alaoui H, Hassan OU, Yang YW, Buchanan P (2015) Lung cancer: biology and treatment options. Biochim Biophys Acta (BBA) Revi Cancer 1856(2):189–210

    Article  Google Scholar 

  • Manikandan T, Bharathi N (2016) Lung cancer detection using fuzzy auto-seed cluster means morphological segmentation and SVM classifier. J Med Syst 40(7):181

    Article  Google Scholar 

  • Mirjalili S, Lewis A (2016) The whale optimization algorithm. Adv Eng Softw 95:51–67

    Article  Google Scholar 

  • Mostafa A, Hassanien AE, Houseni M, Hefny H (2017) Liver segmentation in MRI images based on whale optimization algorithm. Multimed Tools Appl 76(23):24931–24954

    Article  Google Scholar 

  • Pare S, Bhandari AK, Kumar A, Singh GK, Khare S (2015) Satellite image segmentation based on different objective functions using genetic algorithm: a comparative study. In: 2015 IEEE international conference on digital signal processing (DSP). IEEE, pp 730–734

  • Parveen SS, Kavitha C (2014) Classification of lung cancer nodules using SVM kernels. Int J Comput Appl 95(25):25–28

    Google Scholar 

  • Preethi BC, Abraham GE (2016) Lung tissue extraction using OTSU thresholding in lung nodule detection from CT images. Lung 2(06):440–446

    Google Scholar 

  • Santos AM, Ode A, Filho C, Silva AC, Nunes RA (2014) Automatic detection of small lung nodules in 3D CT data using Gaussian mixture models, Tsallis entropy and SVM. Eng Appl Artif Intell 36:27–39

    Article  Google Scholar 

  • Sluimer I, Schilham A, Prokop M, Van Ginneken B (2006) Computer analysis of computed tomography scans of the lung: a survey. IEEE Trans Med Imaging 25(4):385–405

    Article  Google Scholar 

  • Thomas RA, Kumar SS (2014) Automatic detection of lung nodules using classifiers. In 2014 international conference on control, instrumentation, communication and computational technologies (ICCICCT). IEEE, pp 705–710

  • Uzelaltinbulat S, Ugur B (2017) Lung tumor segmentation algorithm. Proc Comput Sci 120:140–147

    Article  Google Scholar 

  • Vijaykumar D, Suraj K, Tushar B, Somnath D (2017) Detection of lung cancer tumor in its early stages using image processing techniques. Int J Adv Eng Res Dev 5(2):326–328

    Google Scholar 

  • Zhao F, Xie X (2013) An overview of interactive medical image segmentation. Ann BMVA 2013(7):1–22

    Google Scholar 

Download references

Acknowledgements

Authors would like to thank for the support and valuable time provided by Amity University, Noida.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Amity University, Sector 125, Noida, Uttar Pradesh, India

    Surbhi Vijh & Deepak Gaur

  2. Department of Computer Science and Engineering, National Institute of Technology Warangal, Warangal, Telangana, India

    Sushil Kumar

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  1. Surbhi Vijh
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  2. Deepak Gaur
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  3. Sushil Kumar
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Correspondence to Sushil Kumar.

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Vijh, S., Gaur, D. & Kumar, S. An intelligent lung tumor diagnosis system using whale optimization algorithm and support vector machine. Int J Syst Assur Eng Manag 11, 374–384 (2020). https://doi.org/10.1007/s13198-019-00866-x

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  • DOI: https://doi.org/10.1007/s13198-019-00866-x

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