Advertisement

An intelligent lung cancer diagnosis system using cuckoo search optimization and support vector machine classifier

  • M. PrabukumarEmail author
  • L. Agilandeeswari
  • K. Ganesan
Original Research

Abstract

One of the leading causes of cancer death for both men and women is the lung cancer. The best way to improve the patient’s chances for survival is the early detection of potentially cancerous cells. But, the conventional systems fails to segment the cancerous cells of various types namely, well-circumscribed, juxta-pleural, juxta-vascular and pleural-tail at its early stage (i.e., less than 3 mm) that leads to less classification accuracy. It is also noted that none of the existing systems achieved accuracy more than 98%. In this paper, we propose an optimal diagnosis system not only for early detection of lung cancer nodules and also to improve the accuracy in Fog computing environment. The Fog environment is used for storage of the high volume CT scanned images to achieve high privacy, low latency and mobility support. In our approach, for the accurate segmentation of Region of Interest (ROI), the hybrid technique namely Fuzzy C-Means (FCM) and region growing segmentation algorithms are used. Then, the important features of the nodule of interest such as geometric, texture and statistical or intensity features are extracted. From the above extracted features, the optimal features used for the classification of lung cancer are identified using the Cuckoo search optimization algorithm. Finally, the SVM classifier is trained using these optimal features, which in turn helps us to classify the lung cancer as either of type benign or malignant. The accuracy of the proposed system is tested using Early Lung Cancer Action Program (ELCAP) public database CT lung images. The total sensitivity and specificity attained in our system for the above said database are 98.13 and 98.79% respectively. This results in a mean accuracy of 98.51% for training and testing in a sample of 103 nodules occurring in 50 exams. The rate of false positives per exam was 0.109. Also, a high receiver operating characteristic (ROC) of 0.9962 has been achieved.

Keywords

Fog computing Fuzzy C-means Region growing segmentation Cuckoo search optimization algorithm SVM classifier 

References

  1. Aarthy KP, Ragupathy US (2012) Detection of lung nodule using multiscale wavelets and support vector machine. Int J Soft Comput Eng (IJSCE) 2(3):32–36Google Scholar
  2. Armato III SG, Senskovie WF (2004) Automated lung segmentation for thoracic CT: impact on computer-aided diagnosis. Acad Radiol 11:1011–1021Google Scholar
  3. Armato SG, Gieger ML, Moran CJ, Blackburn JT, Doi K, Macmahan H (1999) Computerized detection of pulmonary nodules on CT scans. Radiographics 19(5):1303–1311Google Scholar
  4. Aubry CB, Hill C, Grenier PA (2007) Management of an incidentally discovered pulmonary nodule. Eur Radiol 17:449–466Google Scholar
  5. Bezdek CJ (1981) Pattern Recognition with fuzzy objective function algorithms. Kluwer Academic Publishers, Norwell, MA (ISBN: 0-306-40671-3) Google Scholar
  6. Cancer Facts and Figures (2014) American cancer society: cancer statistics. http://www.cancer.org
  7. Chaira T, Ray AK (2009) Fuzzy image processing and applications with MATLAB. CRC press, Taylor and Francis publisherGoogle Scholar
  8. Chang C, Lin C (2014) A library for support vector machines. http://www.csie.ntu.edu.tw/cjlin/libsvm/
  9. Civicioglu P, Besdok E (2013) A conceptual comparison of the cuckoo-search, particle swarm optimization, differential evolution and artificial bee colony algorithms. Artif Intell Rev 39(4):315–346Google Scholar
  10. Cristianini N, Shawe-Taylor J (2000) An introduction to support vector machines. Cambridge University PressGoogle Scholar
  11. Da Silva Sousa JRF, Silva AC, De Paiva AC, Nunes RA (2010) Methodology for automatic detection of lung nodules in computerized tomography images. Comput Methods Prog Biomed 98:1–14Google Scholar
  12. Daliric MR (2012) A hybrid automatic system for the diagnosis of lung cancer based on genetic algorithm and fuzzy extreme learning machines. J Med Syst 36:1001–1005Google Scholar
  13. Dehmeshki D, Ye X, Lin X, Validivieso M, Amin H (2007) Automated detection of lung nodules in CT images using shape-based genetic algorithm. Comput Med Imaging Graph 31:408–417Google Scholar
  14. Dehmeshki J, Amin H, Valdivieso M, Ye X (2008) Segmentation of pulmonary nodules in thoracic CT scans: a region growing approach. IEEE Trans Med Imaging 27(4):467–480Google Scholar
  15. Dunn JC (1973) A fuzzy relative of the ISODATA process and its use in detecting compact well-separated clusters. J Cybern 3:32–57MathSciNetzbMATHGoogle Scholar
  16. El-Baz A, Beache GM, Gimel’farb G, Suzuki K, Okada K, Elnakib A, Soliman A, Abdollahi B. (2013). Computer-aided diagnosis systems for lung cancer: challenges and methodologies. Int J Biomed Imaging 1–46Google Scholar
  17. ELCAP Public Lung Image Database (2014) http://www.via.cornell.edu/lungdb.html. Accessed 15.19, 2014
  18. Engin Avci (2012). A new expert system for diagnosis of lung cancer: GDA–LS_SVM. J Med Syst 36(3):2005–2009Google Scholar
  19. Gadekallu TR, Khare N (2017) Cuckoo search optimized reduction and fuzzy logic classifier for heart disease and diabetes prediction. Int J Fuzzy Syst Appl (IJFSA) 6(2):25–42Google Scholar
  20. Giger ML, Bae KT, MacMahon. H (1994) Computerized detection of pulmonary nodules in computed tomography images. Invest Radiol 29(4):459–465Google Scholar
  21. Girvin F, Ko JP (2008) Pulmonary nodules: detection, assessment, and CAD [J]. AJR Am J Roentgenol 191(4):1057–1069Google Scholar
  22. Gomathi M, Thangaraj P (2010). A new approach to lung image segmentation using fuzzy possibilistic C-means algorithm. Int J Comput Sci Inf Secur 7(3):222–228Google Scholar
  23. Gomathi M, Thangaraj P (2012) An effective classification of benign and malignant nodules using support vector machine. J Global Res Comput Sci 3(7):6–9Google Scholar
  24. Gonzalez RC, Woods RE (2008) Digital image processing, 2nd edn. Prentice hall, Englewood Cliffs, NJGoogle Scholar
  25. Gorodkin J (2004) Comparing two K-category assignments by a K-category correlation coefficient. Comput Biol Chem 28(5–6):367–374zbMATHGoogle Scholar
  26. Gurcan MN, Sahiner B, Petrick N, Chan HP, Kazerooni EA, Cascade PN, Hadjiiski LM (2002) Lung nodule detection on thoracic computed tomography images: preliminary evaluation of a computer-aided diagnosis system. Med Phys 29(11):2552–2558Google Scholar
  27. Hall LO, Bensaid AM, Clarke LP, Velthuizen RP, Silbiger MS, Bezdek JC (1992) A comparison of neural network and fuzzy clustering techniques in segmenting magnetic resonance images of the brain. IEEE Trans Neural Netw 3(5):672–682Google Scholar
  28. Hashemi A, Pilevar AH, Rafeh R (2013) Mass detection in lung CT images using region growing segmentation and decision making based on fuzzy inference system and artificial neural network. Int J Image Graph Signal Process (IJIGSP) 6(1):1–8Google Scholar
  29. Henschke CJ, McCauley DI, Yankelevitz DF, Naidich DP, McGuinness G, Miettinen OS, Libby DM, Pasmantier MW, Koizumi J, Altorki K, Smith JP (1999) Early lung cancer action project: overall design and findings from baseline screening. Lancet 354(9173):99–105Google Scholar
  30. Hoffman EA, McLennan G (1997) Assessment of the pulmonary structure–function relationship and clinical outcomes measures: quantitative volumetric CT of the lung. Acad Radiol 4(11):758–776Google Scholar
  31. Homma N (2011) ct image based computer-aided lung cancer diagnosis, theory and applications of CT imaging and analysis. In: Homma N (ed) InTech. http://www.intechopen.com/books/theory-and-applications-of-ct-imaging-and-analysis/ct-image-basedcomputer-aided-lung-cancer-diagnosis. (ISBN: 978-953-307-234-0)
  32. Hong H, Lee J, Yim Y (2008) Automatic lung nodule matching on sequential CT images. Comput Biol Med 38:623–634Google Scholar
  33. Hsu CW, Chang CC, Lin CJ (2003) A practical guide to support vector machines. Department of Computer Science and Information Engineering, National Taiwan UniversityGoogle Scholar
  34. Hu S, Hoffman EA, Reinhardt JM (2001) Accurate lung segmentation for accurate quantization of volumetric X-ray CT images. IEEE Trans Med Imaging 20(6):490–498Google Scholar
  35. International early lung cancer action program (I-ELCAP) (2006) Survival of patients with stage I lung cancer detected on CT screening. NEM, 355(17):1763–1771Google Scholar
  36. Jemal A, Murray T, Ward E, Samuels A, Tiwari RC, Ghafoor A, Feuer EJ, Thun MJ (2005) Cancer statistics. CA Cancer J Clin 55:10–30Google Scholar
  37. Jinsa K, Gunavathi K (2014) Lung cancer classification using neural networks for CT images. Comput Methods Programs Biomed 113:202–209Google Scholar
  38. Karthikeyan C, Ramadoss B, Baskar S (2012) Segmentation algorithm for ct images using morphological operation and artificial neural network. Int J Signal Process Image Process Patt Recognit 5(2):115–122Google Scholar
  39. 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):19–25Google Scholar
  40. Koltchinskii V, Panchenko D (2002) Empirical margin distributions and bounding the generalization error of combined classifiers. Ann Stat 30(1):1–50MathSciNetzbMATHGoogle Scholar
  41. Kostis WJ, Reeves AP, Yankelevitz DF, Henschke CI (2003) Three dimensional segmentation and growth-rate estimation of small pulmonary nodules in helical CT images. IEEE Tans Med Imag 22(10):1259–1273Google Scholar
  42. Krishnan MMR, Chakraborty C, Paul RR, Ray AK (2012) Hybrid segmentation, characterization and classification of basal cell nuclei from histopathological images of normal oral mucosa and oral sub mucous fibrosis. Expert Syst Appl 39(1):1062–1077Google Scholar
  43. Krishnana MMR, Pal M, Bomminayuni SK, Chakraborty C, Paul RR, Chatterjee J, Ray AK (2009) Automated classification of cells in sub-epithelial connective tissue of oral sub-mucous fibrosis—an SVM based approach. Comput Biol Med 39(12):1096–1104Google Scholar
  44. Kubota T, Jerebko AK, Dewan M, Salganicoff, Krishnan MA (2011) Segmentation of pulmonary nodules of various densities with morphological approaches and convexity models. Med Image Anal 15(1):133–154Google Scholar
  45. Kuhnigk JM, Dicken V, Bornemann L, Bakai A, Wormanns D, Krass S (2006) Morphological segmentation and partial volume analysis for volumetry of solid pulmonary lesions in thoracic CT scans. IEEE Trans Med Imaging 25(6):490–498Google Scholar
  46. Lee SLA, Kouzani AZ, Hu EJ (2010) Random forest based lung nodule classification aided by clustering. Comput Med Imaging Graph 34:535–542Google Scholar
  47. Lemjabbar-Alaoui H, Hassan OUI, Yang YW, Buchanan P (2015) Lung cancer: biology and treatment options. Biochem Biophys Acta 1856:189–210Google Scholar
  48. Leung A, Smithuis R (2007) Solitary pulmonary nodule: benign versus malignant. http://www.radiologyassistant.nl/en/p460f9fcd50637/solitary-pulmonary-nodule-benign-versus-malignant.html
  49. Li Y, Shen Y (2010) Fuzzy c-means clustering based on spatial neighbourhood information for image segmentation. J Syst Eng Electron 21:323–328Google Scholar
  50. Liu X, Fu H (2014) PSO-based support vector machine with cuckoo search technique for clinical disease diagnoses. Sci World J 2014:1–7Google Scholar
  51. Magalhaes BNS, Silva AFC, Nunes RA, Gattass M (2012) Automatic segmentation of lung nodules with growing neural gas and support vector machine. Comput Biol Med 42:1110–1121Google Scholar
  52. Micheli A, Baili P, Quinn M, Mugno E, Capocaccia R, Grosclaude P (2003) Life expectancy and cancer survival. The EUROCARE Working Group in the EUROCARE-3 cancer registry areas. Ann Oncol 14(5):V28–V40Google Scholar
  53. Mohanty AK, Senapati MR, Lenka SK (2013) A novel image mining technique for classification of mammograms using hybrid feature selection. Neural Comput Appl 22(6):1151–1161Google Scholar
  54. Motohiro A, Ueda H, Komatsu H, Yanai N, Mori T (2002) Prognosis of non-surgically treated, clinical stage I lung cancer patients in Japan. Lung Cancer 36:65–69Google Scholar
  55. Okumura T, Miwa T, Kako J, Yamamoto S, Matsumoto M, Tateno Y, Iinuma T, Matsumoto T (1998). Variable-N-Quoit filter applied for automatic detection of lung cancer by X-ray CT. Proc Comput Assist Radiol 242–247 (in Japanese) Google Scholar
  56. Otsu N (1979) A threshold selection method from gray-level histograms. IEEE Trans Sys Man Cyber 9(1):62–66Google Scholar
  57. Patil SA, Udupi VR, Kane CD, Wasif AI, Desai JV, Jadhav AN (2009) Geometrical and texture feature estimation of lung cancer and TB image using chest X-ray database. International Conference on Biomedical and Pharmaceutical Engineering (ICBPE ‘09), pp 1–7Google Scholar
  58. Prokop M, Galanshi M (2003). Spiral and multislice computed tomography of the body. Thime medical publishers, StuttgartGoogle Scholar
  59. Pu J, Leader JK, Zheng B (2009) A computational geometry approach to automated pulmonary fissure segmentation in CT examinations. IEEE Trans Med Imaging 28(5):710–719Google Scholar
  60. Roman R, Lopez J, Mambo M (2016) Mobile edge computing., Fog et al.: A survey and analysis of security threats and challenges. Future Gen Comput Syst (Article in online) Google Scholar
  61. Rouhi R, Jafari M (2016) Classification of benign and malignant breast tumors on hybrid level set segmentation. Experts Syst Appl 46:45–59Google Scholar
  62. 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 36:27–39Google Scholar
  63. Schilham AMR, Ginneken BV, Loog M (2006) A Computer-aided diagnosis system for detection of lung nodules in chest radiographs with an evaluation on a public database. Med Image Anal 10:247–258Google Scholar
  64. Shaik Parveen S, Kavitha C (2014) Classification of lung cancer nodules using SVM Kernels. Int J Comput Appl 95(25):25–28Google Scholar
  65. Sharma D, Jindal G (2011) Identifying lung cancer using image processing techniques. In: International Conference on Computational Techniques and Artificial Intelligence (ICCTAI’2011), pp 115–122Google Scholar
  66. Shehab M, Khader AT, Al-Betar MA (2017) A survey on applications and variants of the cuckoo search algorithm. Appl Soft Comput J (Article in press) Google Scholar
  67. Shen S, Bui AA, Cong J, Hsu W (2015) An automated lung segmentation approach using bidirectional chain codes to improve nodule detection accuracy. Comput Biol Med 57:139–149Google Scholar
  68. Singh AAG, Jebamalar Leavline E, Valliyappan K, Srinivasan M (2015) Enhancing the performance of classifier using particle swarm optimization (PSO) based dimensionality reduction. Int J Energy Inf Commun 6(5):19–26Google Scholar
  69. Sluimer I, Schilham A, Prokop M, Ginneken BV (2006) Computer Analysis of computer tomography scans of the lung: a survey. IEEE Trans Med Imag 25(4):385–405Google Scholar
  70. Subashini MM, Sahoo SK, Sunil V, Easwaran S (2016) A non-invasive methodology for the grade identification of astrocytoma using image processing and artificial intelligence techniques. Expert Syst Appl 43:186–196Google Scholar
  71. Sun S, Ren H, Kang Y, Zhao H (2011) Lung nodule detection by GA and SVM. J Syst Simul 23(3):497–502Google Scholar
  72. Sun T, Wanga J, Li X, Lv P, Liu F, Luo Y, Gao Q, Zhu H, Guo X (2013). Comparative evaluation of support vector machines for computer aided diagnosis of lung cancer in CT based on a multi-dimensional data set. Comput Methods Progr Biomed 111(2):519–524Google Scholar
  73. Tay D, Pohb CL, Goha C, Kitney RI (2014) A biological continuum based approach for efficient clinical classification. J Biomed Inform 47:28–38Google Scholar
  74. Vapnik VN, Kotz S (2006). Estimation of dependences based on empirical data. Springer (ISBN: 0-387-30865-2) Google Scholar
  75. Wafa M, Zagrouba E (2009) Improved fuzzy-c-means for noisy image segmentation. SIGMAP 2009, pp 74–78Google Scholar
  76. Xin-She Y (2014) Cuckoo search and firefly algorithm theory and applications. SpringerGoogle Scholar
  77. Yang XS, Deb S (2009) Cuckoo search via Levy flights. In: Proc. of World Congress on Nature and Biologically Inspired Computing (NaBIC 2009), pp 210–214Google Scholar
  78. Yang XS, Deb S (2010) Engineering optimization by cuckoo search. Int J Math Model Num Optim 1(4):330–343zbMATHGoogle Scholar
  79. Yi S, Qin Z, Li Q (2015) Security and privacy issues of fog computing: a survey. In: International conference on wireless algorithms, systems, and applications. Springer, pp 685–695Google Scholar
  80. Zhao B, Yankelevitz D, Reeves A, Henschke C (1999) Two-dimensional multi-criterion segmentation of pulmonary nodules in helical CT images. Med Phys 26(6):889–895Google Scholar
  81. Zhou S, Chenga Y, Tamura S (2014) Automated lung segmentation and smoothing techniques for inclusion of juxta pleural nodules and pulmonary vessels on chest CT images. Biomed Signal Process Control 13:62–70Google Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2017

Authors and Affiliations

  1. 1.School of Information Technology and EngineeringVIT UniversityVelloreIndia
  2. 2.TIFAC - CORE in Automotive InfotronicsVIT UniversityVelloreIndia

Personalised recommendations