Skip to main content
SpringerLink
Log in
Book cover

International Conference on ISMAC in Computational Vision and Bio-Engineering

ISMAC 2018: Proceedings of the International Conference on ISMAC in Computational Vision and Bio-Engineering 2018 (ISMAC-CVB) pp 1921–1930Cite as

Breast Cancer Recognition by Support Vector Machine Combined with Daubechies Wavelet Transform and Principal Component Analysis

  • Conference paper
  • First Online:

Part of the book series: Lecture Notes in Computational Vision and Biomechanics ((LNCVB,volume 30))

  • The original version of this chapter was revised: The Corresponding Author function has been assigned to Mackenzie Brown and the wrong labeling of images in figure 1 has been corrected. The correction to this chapter is available at https://doi.org/10.1007/978-3-030-00665-5_178

Abstract

The method of identifying the abnormal mammary gland tumor images was presented in order to assist the medical staff to find the patients with breast diseases accurately and timely. Db2 wavelet transform and principal component analysis (select the optimal threshold) is used to extract the effective features, support vector machine (set appropriate penalty parameter) is used to classify health and diseased samples, and 10-fold cross-validation is used to verify the classification result. The experimental results show that the method is feasible, the average sensitivity is 83.10 ± 1.91%, the average specificity is 82.60 ± 4.50%, and the average accuracy is 82.85 ± 2.21%.

This is a preview of subscription content, log in via an institution.

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   44.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD   59.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

Change history

  • 25 March 2023

    The Corresponding Author function has been assigned to Mackenzie Brown and the wrong labeling of images in figure 1 has been corrected.

    The correction chapter and the book has been updated with the changes.

References

  1. Xu Y, Zhu Q, Wang J (2012) Breast cancer diagnosis based on a kernel orthogonal transform. Neural Comput Appl 21(8):1865–1870

    Google Scholar 

  2. Senapati MR et al (2013) Local linear wavelet neural network for breast cancer recognition. Neural Comput Appl 22(1):125–131

    Google Scholar 

  3. Uzer MS, Inan O, Yilmaz N (2013) A hybrid breast cancer detection system via neural network and feature selection based on SBS, SFS and PCA. Neural Comput Appl 23(3):719–728

    Google Scholar 

  4. Azar AT, El-Said SA (2014) Performance analysis of support vector machines classifiers in breast cancer mammography recognition. Neural Comput Appl 24(5):1163–1177

    Google Scholar 

  5. Zhou XX, Zhang GS (2016) Detection of abnormal MR brains based on wavelet entropy and feature selection. IEEJ Trans Electr Electron Eng 11(3):364–373

    Google Scholar 

  6. Yang JQ et al (2016) A novel compressed sensing method for magnetic resonance imaging: exponential wavelet iterative shrinkage-thresholding algorithm with random shift. Int J Biomed Imaging. Article ID. 9416435

    Google Scholar 

  7. Sun P (2016) Preliminary research on abnormal brain detection by wavelet-energy and quantum-behaved PSO. Technol Health Care 24(s2):S641–S649

    MathSciNet  Google Scholar 

  8. Yang M (2016) Dual-tree complex wavelet transform and twin support vector machine for pathological brain detection. Appl Sci 6(6): Article ID. 169

    Google Scholar 

  9. Zhou X-X (2016) Comparison of machine learning methods for stationary wavelet entropy-based multiple sclerosis detection: decision tree, k-nearest neighbors, and support vector machine. Simulation 92(9):861–871

    Google Scholar 

  10. Lu HM (2016) Facial emotion recognition based on biorthogonal wavelet entropy, fuzzy support vector machine, and stratified cross validation. IEEE Access 4:8375–8385

    Google Scholar 

  11. Nayak DR (2017) Detection of unilateral hearing loss by stationary wavelet entropy. CNS & Neurol Disord-Drug Targets 16(2):15–24

    Google Scholar 

  12. Wang S-H (2016) Single slice based detection for Alzheimer’s disease via wavelet entropy and multilayer perceptron trained by biogeography-based optimization. Multimed Tools Appl. https://doi.org/10.1007/s11042-016-4222-4

  13. Li Y, Cattani C (2017) Detection of dendritic spines using wavelet packet entropy and fuzzy support vector machine. CNS & Neurol Disord-Drug Targets 16(2):116–121

    Google Scholar 

  14. Li P, Liu G (2017) Pathological brain detection via wavelet packet tsallis entropy and real-coded biogeography-based optimization. Fundam Inform 151(1–4):275–291

    MathSciNet  Google Scholar 

  15. Chen M (2016) Morphological analysis of dendrites and spines by hybridization of ridge detection with twin support vector machine. PeerJ 4:e2207

    Google Scholar 

  16. Chen S, Yang J-F, Phillips P (2015) Magnetic resonance brain image classification based on weighted-type fractional Fourier transform and nonparallel support vector machine. Int J Imaging Syst Technol 25(4):317–327

    Google Scholar 

  17. Dong Z (2014) Classification of Alzheimer disease based on structural magnetic resonance imaging by kernel support vector machine decision tree. Prog Electromagn Res 144:171–184

    Google Scholar 

  18. Ji G (2013) An MR brain images classifier system via particle swarm optimization and kernel support vector machine. Sci World J (130134)

    Google Scholar 

  19. Wu L (2012) Classification of fruits using computer vision and a multiclass support vector machine. Sensors 12(9):12489–12505

    Google Scholar 

  20. Wu L (2012) An MR brain images classifier via principal component analysis and kernel support vector machine. Prog Electromagn Res 130:369–388

    Google Scholar 

  21. The mini-MIAS database of mammograms (2018) Available from http://peipa.essex.ac.uk/info/mias.html

  22. Gorriz JM (2017) Multivariate approach for Alzheimer’s disease detection using stationary wavelet entropy and predator-prey particle swarm optimization. J Alzheimer’s Dis https://doi.org/10.3233/jad-170069

  23. Phillips P (2018) Intelligent facial emotion recognition based on stationary wavelet entropy and Jaya algorithm. Neurocomputing 272:668–676

    Google Scholar 

  24. Han L (2018) Identification of Alcoholism based on wavelet Renyi entropy and three-segment encoded Jaya algorithm. Complexity 2018(3198184):13

    MathSciNet  MATH  Google Scholar 

  25. Zhou X-X et al (2016) Combination of stationary wavelet transform and kernel support vector machines for pathological brain detection. Simulation 92(9):827–837

    Google Scholar 

  26. Atangana A (2018) Application of stationary wavelet entropy in pathological brain detection. Multimed Tools Appl 77(3):3701–3714

    Google Scholar 

  27. Chen Y, Chen X-Q (2016) Sensorineural hearing loss detection via discrete wavelet transform and principal component analysis combined with generalized eigenvalue proximal support vector machine and Tikhonov regularization. Multimed Tools Appl 77(3):3775–3793

    Google Scholar 

  28. Chen Y, Lu H (2018) Wavelet energy entropy and linear regression classifier for detecting abnormal breasts. Multimed Tools Appl 77(3):3813–3832

    MathSciNet  Google Scholar 

  29. Zhan TM, Chen Y (2016) Multiple sclerosis detection based on biorthogonal wavelet transform, RBF kernel principal component analysis, and logistic regression. IEEE Access 4:7567–7576

    Google Scholar 

  30. Schimit PHT, Pereira FH (2018) Disease spreading in complex networks: a numerical study with principal component analysis. Expert Syst Appl 97:41–50

    Google Scholar 

  31. Zhao G (2017) Polarimetric synthetic aperture radar image segmentation by convolutional neural network using graphical processing units. J Real-Time Image Process https://doi.org/10.1007/s11554-017-0717-0

  32. Muhammad K (2017) Image based fruit category classification by 13-layer deep convolutional neural network and data augmentation. Multimedia Tools Appl. https://doi.org/10.1007/s11042-017-5243-3

  33. Tang C (2017) Twelve-layer deep convolutional neural network with stochastic pooling for tea category classification on GPU platform. Multimedia Tools Appl. https://doi.org/10.1007/s11042-018-5765-3

  34. Lv Y-D (2018) Alcoholism detection by data augmentation and convolutional neural network with stochastic pooling. J Med Syst 42(1):2

    Google Scholar 

  35. Gupta A, Kumar D (2018) Beyond the limit of assignment of metabolites using minimal serum samples and H-1 NMR spectroscopy with cross-validation by mass spectrometry. J Pharm Biomed Anal 151:356–364

    Google Scholar 

  36. Hou X-X (2017) Seven-layer deep neural network based on sparse autoencoder for voxelwise detection of cerebral microbleed. Multimedia Tools Appl. https://doi.org/10.1007/s11042-017-4554-8

  37. Jia W (2017) Five-category classification of pathological brain images based on deep stacked sparse autoencoder. Multimedia Tools Appl. https://doi.org/10.1007/s11042-017-5174-z

  38. Jia W (2017) Three-category classification of magnetic resonance hearing loss images based on deep autoencoder. J Med Syst 41(10):165

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Computer Science and Technology, Nanjing Normal University, Nanjing, 210023, Jiangsu, China

    Fangyuan Liu

  2. School of Engineering, Edith Cowan University, Joondalup, WA, 6027, Australia

    Mackenzie Brown

Authors
  1. Fangyuan Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Mackenzie Brown
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Mackenzie Brown .

Editor information

Editors and Affiliations

  1. SCAD Institute of Technology, Palladam, India

    Durai Pandian

  2. Department of Electrical and Computer Engineering, Ryerson University, Toronto, ON, Canada

    Xavier Fernando

  3. School of Computer and Security Science, Edith Cowan University, Joondalup, WA, Australia

    Zubair Baig

  4. Wenzhou Medical University, Wenzhou, China

    Fuqian Shi

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Liu, F., Brown, M. (2019). Breast Cancer Recognition by Support Vector Machine Combined with Daubechies Wavelet Transform and Principal Component Analysis. In: Pandian, D., Fernando, X., Baig, Z., Shi, F. (eds) Proceedings of the International Conference on ISMAC in Computational Vision and Bio-Engineering 2018 (ISMAC-CVB). ISMAC 2018. Lecture Notes in Computational Vision and Biomechanics, vol 30. Springer, Cham. https://doi.org/10.1007/978-3-030-00665-5_177

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-00665-5_177

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-00664-8

  • Online ISBN: 978-3-030-00665-5

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation