Breast Cancer Classification Using Tetrolet Transform Based Energy Features and K-Nearest Neighbor Classifier

  • A. Amjath AliEmail author
  • Suman Mishra
  • Bhasker Dappuri
Part of the Intelligent Systems Reference Library book series (ISRL, volume 172)


The cancer that develops in the breast tissue is referred to as breast cancer. The cancer in the breast could be sometimes symptomatic and are identified by self-examination of the breast or by a physician, whereas in certain cases there could be no symptoms at all. However, signs like a lump in the breast, change of size of the breast, dimpling and fluid discharge from the nipple are some of the symptoms which are cause of grave concern. The early diagnosis of the disease is the key to combat the deadly disease paving the path for hope of life. Mammography is a very popular technique that is used for the early diagnosis of breast cancer. In this study, a technique for breast cancer classification in digitized mammogram is put-forth employing tetrolet transform based energy features and K-Nearest Neighbor (KNN) classifier. The breast mammogram images of benign and malignant category are decomposed into sub-band coefficients using tetrolet transform and the energy features are extracted. These extracted features are given as input to the KNN classifier. Results show better classification accuracy in the breast cancer images using tetrolet transform based energy features and KNN classifier.


Breast cancer Tetrolet transform Energy features KNN classifier 


  1. 1.
    Boudraa, S., Melouah, A., Merouani, H.F.: Deep texture representation for breast mass classification. In: 2018 International Conference on Signal, Image, Vision and their Applications (SIVA), pp. 1–4, IEEE (2018).
  2. 2.
    Liu, B., Li, X., Li, J., Li, Y., Lang, J., Gu, R., Wang, F.: Comparison of machine learning classifiers for breast cancer diagnosis based on feature selection. In: 2018 IEEE International Conference on Systems, Man, and Cybernetics (SMC), pp. 4399–4404, IEEE (2018).
  3. 3.
    Alkhaleefah, M., Wu, C.C.: A hybrid CNN and RBF-based SVM approach for breast cancer classification in mammograms. In: 2018 IEEE International Conference on Systems, Man, and Cybernetics (SMC), pp. 894–899, IEEE (2018).
  4. 4.
    Chen, D., Qian, G., Pan, Q.: Breast cancer classification with electronic medical records using hierarchical attention bidirectional networks. In: 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 983–988, IEEE (2018).
  5. 5.
    Soliman, O.O., Sweilam, N.H., Shawky, D.M.: Automatic breast cancer detection using digital thermal images. In: 2018 9th Cairo International Biomedical Engineering Conference (CIBEC), pp. 110–113, IEEE (2018).
  6. 6.
    Elelimy, E., Mohamed, A.A.: Towards automatic classification of breast cancer histopathological image. In: 2018 13th International Conference on Computer Engineering and Systems (ICCES), pp. 299–306, IEEE (2018).
  7. 7.
    Salem, M.A.M.: Mammogram-based cancer detection using deep convolutional neural networks. In: 2018 13th International Conference on Computer Engineering and Systems (ICCES), pp. 694–699, IEEE (2018).
  8. 8.
    Kabir, S.M., Bhuiyan, M.I.H.: Classification of breast tumour in Contourlet transform domain. In: 2018 10th International Conference on Electrical and Computer Engineering (ICECE), pp. 289–292, IEEE (2018).
  9. 9.
    Adeshina, S.A., Adedigba, A.P., Adeniyi, A.A., Aibinu, A.M.: Breast cancer histopathology image classification with deep convolutional neural networks. In: 2018 14th International Conference on Electronics Computer and Computation (ICECCO), pp. 206–212, IEEE (2018).
  10. 10.
    Angara, S., Robinson, M., Guillén-Rondon, P.: Convolutional neural networks for breast cancer histopathological image classification. In: 2018 4th International Conference on Big Data and Information Analytics (BigDIA), pp. 1–6, IEEE (2018).
  11. 11.
    Dua, S., Acharya, U.R., Chowriappa, P., Sree, S.V.: Wavelet-based energy features for glaucomatous image classification. IEEE Trans. Inf. Technol. Biomed. 16(1), 80–87 (2012). Scholar
  12. 12.
    Ramesh, G.P., Malini, M., Professor, P.G.: An efficacious method of cup to disc ratio calculation for glaucoma diagnosis using super pixel. Int. J. Comput. Sci. Eng. Commun. 2(3) (2014)Google Scholar

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© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Department of Electrical and Electronics EngineeringIbra College of TechnologyIbraOman
  2. 2.CMR Engineering CollegeHyderabadIndia

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