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A new Bio-CAD system based on the optimized KPCA for relevant feature selection

  • Syrine Neffati
  • Khaoula Ben Abdellafou
  • Okba Taouali
  • Kais Bouzrara
ORIGINAL ARTICLE
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Abstract

Computer-aided design (CAD) systems are known to be used in manufacturing, modern engineering design and modeling. New applications to this technology have been created in other fields, such as biomedicine and health informatics, due to the remarkable improvements achieved in recent years. This paper proposes a new biomedical computer-aided design (Bio-CAD) system based on an optimized kernel principal component analysis (OKPCA) for brain tumor diagnosis entitled CAD-OKPCA. The concept of this method consists of reducing the complexity involved in the medical images by selecting only the relevant features using the OKPCA, while maintaining good classification rates. Three databases have been used to validate the proposed CAD-OKPCA method and the results were satisfactory.

Keywords

CAD Optimization KPCA OKPCA Feature reduction 

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References

  1. 1.
    Mittermeyer SA, Njuguna JA, Alcock JR (2011) Product–service systems in health care: case study of a drug–device combination. Int J Adv Manuf Technol 52(9-12):1209–1221CrossRefGoogle Scholar
  2. 2.
    Zhang Y, Dong Z, Wu L, Wang S (2011) A hybrid method for MRI brain image classification. Expert Syst Appl 38(8):10049–10053CrossRefGoogle Scholar
  3. 3.
    Giannatsis J, Dedoussis V (2009) Additive fabrication technologies applied to medicine and health care: a review. Int J Adv Manuf Technol 40(1-2):116–127CrossRefGoogle Scholar
  4. 4.
    Chaplot S, Patnaik LM, Jagannathan NR (2006) Classification of magnetic resonance brain images using wavelets as input to support vector machine and neural network. Biomed Signal Process Control 1(1):86–92CrossRefGoogle Scholar
  5. 5.
    Tagluk ME, Akin M, Sezgin N (2010) Classification of sleep apnea by using wavelet transform and artificial neural networks. Expert Syst Appl 37(2):1600–1607CrossRefGoogle Scholar
  6. 6.
    Olsson D (2011) Applications and implementation of kernel principal component analysis to special data sets. Master’s Thesis Report, University of FloridaGoogle Scholar
  7. 7.
    Taouali O, Jaffel I, Lahdhiri H, Harkat MF, Messaoud H (2016) New fault detection method based on reduced kernel principal component analysis (RKPCA). Int J Adv Manuf Technol 85(5-8):1547–1552CrossRefGoogle Scholar
  8. 8.
    Yeh JY, Fu JC (2008) A hierarchical genetic algorithm for segmentation of multi-spectral human-brain MRI. Expert Syst Appl 34(2):1285–1295CrossRefGoogle Scholar
  9. 9.
    Patil NS, Shelokar PS, Jayaraman VK, Kulkarni BD (2005) Regression models using pattern search assisted least square support vector machines. Chem Eng Res Des 83(8):1030–1037CrossRefGoogle Scholar
  10. 10.
    Xu Y, Guo Y, Xia L, Wu Y (2008) An support vector regression based nonlinear modeling method for SiC MESFET. Prog Electromagn Res 2:103–114CrossRefGoogle Scholar
  11. 11.
    Li D, Yang W, Wang S (2010) Classification of foreign fibers in cotton lint using machine vision and multi-class support vector machine. Comput Electron Agric 74(2):274–279CrossRefGoogle Scholar
  12. 12.
    Gomes TA, Prudêncio RB, Soares C, Rossi AL, Carvalho A (2012) Combining meta-learning and search techniques to select parameters for support vector machines. Neurocomputing 75(1):3–13CrossRefGoogle Scholar
  13. 13.
    Neffati S, Taouali O (2017) An MR brain images classification technique via the Gaussian radial basis kernel and SVM, In: Sciences and techniques of automatic control and computer engineering (STA), 18th edn, pp 611-616Google Scholar
  14. 14.
    Mallat SG (1989) A theory for multiresolution signal decomposition: the wavelet representation. IEEE Trans Pattern Anal Mach Intell 11(7):674–693CrossRefzbMATHGoogle Scholar
  15. 15.
    Zhang Y, Wu L (2012) An MR brain images classifier via principal component analysis and kernel support vector machine. Prog Electromagn Res 130:369–388CrossRefGoogle Scholar
  16. 16.
    Zhang Y, Wang S, Wu L (2010) A novel method for magnetic resonance brain image classification based on adaptive chaotic PSO. Prog Electromagn Res 109:325–343CrossRefGoogle Scholar
  17. 17.
    Fazai R, Taouali O, Harkat MF, Bouguila N (2016) A new fault detection method for nonlinear process monitoring. Int J Adv Manuf Technol 87(9-12):3425–3436CrossRefGoogle Scholar
  18. 18.
    Bishop CM (2006) Continuous latent variables. In: Pattern recognition and machine learning, Springer, pp 559–599Google Scholar
  19. 19.
    Quan W (2014) Kernel Principal Component Analysis and its Applications in Face Recognition and Active Shape Models, Computing Research Repository, 2014Google Scholar
  20. 20.
    Li H, Feng X, Cao L, Zhang C, Tang C, Li E, Chen X (2016) Heartbeat classification using different classifiers with non-linear feature extraction. Trans Inst Meas Control 38(9):1033–1040CrossRefGoogle Scholar
  21. 21.
    Han T, Jiang D, Zhao Q, Wang L, Yin K (2018) Comparison of random forest, artificial neural networks and support vector machine for intelligent diagnosis of rotating machinery. Trans Inst Meas Control, 1–13Google Scholar
  22. 22.
    Weinberger KQ, Sha F, Saul LK (2004, July) Learning a kernel matrix for nonlinear dimensionality reduction. In: Proceedings of the twenty-first international conference on machine learning, ACM, p 106Google Scholar
  23. 23.
    Vapnik V (1998) Statistical learning theory. Wiley, New YorkzbMATHGoogle Scholar
  24. 24.
    Taouali O, Elaissi I, Messaoud H (2014) Hybrid kernel identification method based on support vector regression and regularisation network algorithms. IET Signal Proc 8(9):981–989CrossRefGoogle Scholar
  25. 25.
    Martiskainen P, Järvinen M, Skön JP, Tiirikainen J, Kolehmainen M, Mononen J (2009) Cow behaviour pattern recognition using a three-dimensional accelerometer and support vector machines. Appl Anim Behav Sci 119(1):32–38CrossRefGoogle Scholar
  26. 26.
    Acevedo-Rodríguez J, Maldonado-Bascón S, Lafuente-Arroyo S, Siegmann P, López-Ferreras F (2009) Computational load reduction in decision functions using support vector machines. Signal Process 89 (10):2066–2071CrossRefzbMATHGoogle Scholar
  27. 27.
    Schölkopf B, Smola AJ (2002) Learning with kernels: support vector machines, regularization, optimization, and beyond, MIT pressGoogle Scholar
  28. 28.
    Bermejo S, Monegal B, Cabestany J (2007) Fish age categorization from otolith images using multi-class support vector machines. Fish Res 84(2):247–253CrossRefGoogle Scholar
  29. 29.
    Das S, Chowdhury M, Kundu MK (2013) Brain MR image classification using multiscale geometric analysis of ripplet. Prog Electromagn Res 137:1–17CrossRefGoogle Scholar
  30. 30.
    Segreto T, Karam S, Teti R (2017) Signal processing and pattern recognition for surface roughness assessment in multiple sensor monitoring of robot-assisted polishing. Int J Adv Manuf Technol 90(1-4):1023–1033CrossRefGoogle Scholar
  31. 31.
    Bhat NN, Dutta S, Vashisth T, Pal S, Pal SK, Sen R (2016) Tool condition monitoring by SVM classification of machined surface images in turning. Int J Adv Manuf Technol 83(9-12):1487– 1502CrossRefGoogle Scholar
  32. 32.
    Nayak DR, Dash R, Majhi B (2016) Brain MR image classification using two-dimensional discrete wavelet transform and AdaBoost with random forests. Neurocomputing 177:188– 197CrossRefGoogle Scholar
  33. 33.
    El-Dahshan ESA, Hosny T, Salem ABM (2010) Hybrid intelligent techniques for MRI brain images classification. Digital Signal Process 20(2):433–441CrossRefGoogle Scholar
  34. 34.
    Saritha M, Joseph KP, Mathew AT (2013) Classification of MRI brain images using combined wavelet entropy based spider web plots and probabilistic neural network. Pattern Recogn Lett 34(16):2151–2156CrossRefGoogle Scholar
  35. 35.
    El-Dahshan ESA, Mohsen HM, Revett K, Salem ABM (2014) Computer-aided diagnosis of human brain tumor through MRI: a survey and a new algorithm. Expert systems with Applications 41(11):5526–5545CrossRefGoogle Scholar
  36. 36.
    Wang S, Zhang Y, Dong Z, Du S, Ji G, Yan J, Phillips P (2015) Feed-forward neural network optimized by hybridization of PSO and ABC for abnormal brain detection. Int J Imaging Syst Technol 25(2):153–164CrossRefGoogle Scholar
  37. 37.
    Zhang Y, Wang S, Dong Z, Phillip P, Ji G, Yang J (2015) Pathological brain detection in magnetic resonance imaging scanning by wavelet entropy and hybridization of biogeography-based optimization and particle swarm optimization. Prog Electromagn Res 152:41–58CrossRefGoogle Scholar
  38. 38.
    ZZhang Y, Dong Z, Wang S, Ji G, Yang J (2015) Preclinical diagnosis of magnetic resonance (MR) brain images via discrete wavelet packet transform with Tsallis entropy and generalized eigenvalue proximal support vector machine (GEPSVM). Entropy 17(4):1795–1813CrossRefGoogle Scholar
  39. 39.
    Maitra M, Chatterjee A (2006) A Slantlet transform based intelligent system for magnetic resonance brain image classification. Biomed Signal Process Control 1(4):299–306CrossRefGoogle Scholar
  40. 40.
    Jaffel I, Taouali O, Harkat MF, Messaoud H (2018) Fault detection and isolation in nonlinear systems with partial Reduced Kernel Principal Component Analysis method. Trans Inst Meas Control 40(4):1289–1296CrossRefGoogle Scholar
  41. 41.
    Sun W, Darling A, Starly B, Nam J (2004) Computer aided tissue engineering: overview, scope and challenges. Biotechnol Appl Biochem 39(1):29–47CrossRefGoogle Scholar
  42. 42.
    Jiang S, Ren Z, Wu Z (2014) Mechanistic force modeling and machinability evaluation on MR-compatible materials. Int J Adv Manuf Technol 74(1-4):151–161CrossRefGoogle Scholar
  43. 43.
  44. 44.
    Said M, Fazai R, Abdellafou KB, Taouali O (2018) Decentralized fault detection and isolation using bond graph and PCA methods. Int J Adv Manuf Technol 99(1-4):517–529CrossRefGoogle Scholar

Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2019

Authors and Affiliations

  • Syrine Neffati
    • 1
  • Khaoula Ben Abdellafou
    • 2
  • Okba Taouali
    • 3
  • Kais Bouzrara
    • 1
  1. 1.University of MonastirNational Engineering School of MonastirMonastirTunisia
  2. 2.Universite de Sousse, ISITComMARS Research LaboratoryHammam SousseTunisia
  3. 3.Department of Computer EngineeringFaculty of Computers and Information Technology, University of TabukTabukSaudi Arabia

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