A Review on Detection of Breast Cancer Cells by Using Various Techniques

  • Vanaja Kandubothula
  • Rajyalakshmi Uppada
  • Durgesh NandanEmail author
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1154)


This paper discussed a framework for the detection of breast cancer cells by using various techniques. Dangerous cancer is mostly observed in women’s breast. The mortality rate can be decreased when breast cancer is detected at an early stage. By using different techniques, breast cancer cells can be detected. From the past decade, to detect and identify the stage of the cancer, computer-aided diagnosis (CAD) system has been initiated. This system consists of different steps like preprocessing, nuclei detection, segmentation, feature extraction, and classification to detect breast cancer cells. The approaches and methodologies in each step of the CAD system are applied to the images for cancer cell detection. Classification is done by using different classifiers. Features and classification results of different techniques for various images for detecting breast cancer cells are reviewed.


CAD system Grayscale image RGB image SVM MLP 


  1. 1.
    Abdallah, Y.M., Elgak, S., Zain, H., Rafiq, M., Ebaid, E.A., Elnaema, A.A.: Breast cancer detection using image enhancement and segmentation algorithms. Biomed. Res. 29(20), 3732–3736 (2018)CrossRefGoogle Scholar
  2. 2.
    Angayarkanni, N., Kumar, D., Arunachalam, G.: The application of image processing techniques for detection and classification of cancerous tissue in digital mammograms. J. Pharm. Sci. Res. 8(10), 1179 (2016)Google Scholar
  3. 3.
    Araújo, T., Aresta, G., Castro, E., Rouco, J., Aguiar, P., Eloy, C., Polónia, A., Campilho, A.: Classification of breast cancer histology images using convolutional neural networks. PloS one 12(6), e0177, 544 (2017)Google Scholar
  4. 4.
    Arpana, M., Kiran, P.: Feature extraction values for digital mammograms. Int. J. Soft Comput. Eng. (IJSCE) 4(2), 183–187 (2014)Google Scholar
  5. 5.
    Badawy, S.M., Hefnawy, A.A., Zidan, H.E., GadAllah, M.T.: Breast cancer detection with mammogram segmentation: a qualitative study. Int. J. Adv. Comput. Sci. Appl. (IJACSA) 8(10) (2017)Google Scholar
  6. 6.
    Bagchi, S., Huong, A.: Signal processing techniques and computer-aided detection systems for diagnosis of breast cancer—a review paper (2017)Google Scholar
  7. 7.
    Biswas, S.K., Mia, M.M.A.: Image reconstruction using multi layer perceptron (mlp) and support vector machine (svm) classifier and study of classification accuracy. Int. J. Sci. Technol. Res. 4(2), 226–231 (2015)Google Scholar
  8. 8.
    Chan, A., Tuszynski, J.A.: Automatic prediction of tumour malignancy in breast cancer with fractal dimension. R. Soc. Open Sci. 3(12), 160,558 (2016)Google Scholar
  9. 9.
    Dhas, A.S., Vijikala, V.: An improved cad system for abnormal mammogram image classification using svm with linear kernel (2017)Google Scholar
  10. 10.
    Durgesh, K.S., Lekha, B.: Data classification using support vector machine. J. Theor. Appl. Inf. Technol. 12(1), 1–7 (2010)Google Scholar
  11. 11.
    George, Y.M., Zayed, H.H., Roushdy, M.I., Elbagoury, B.M.: Remote computer-aided breast cancer detection and diagnosis system based on cytological images. IEEE Syst. J. 8(3), 949–964 (2013)CrossRefGoogle Scholar
  12. 12.
    Ghongade, R., Wakde, D.: Computer-aided diagnosis system for breast cancer using rf classifier, pp. 1068–1072 (2017)Google Scholar
  13. 13.
    Jadoon, M.M., Zhang, Q., Haq, I.U., Butt, S., Jadoon, A.: Three-class mammogram classification based on descriptive cnn features. BioMed Res. Int. (2017)Google Scholar
  14. 14.
    Jagdale, S., Kolekara, M.H., Khot, U.P.: Smart sensing using bayesian network for computer aided diagnostic systems. Proc. Comp. Sci. 45, 762–769 (2015)CrossRefGoogle Scholar
  15. 15.
    Jalalian, A., Mashohor, S., Mahmud, R., Karasfi, B., Saripan, M.I.B., Ramli, A.R.B.: Foundation and methodologies in computer-aided diagnosis systems for breast cancer detection, p. 113. Leibniz Research Centre for Working Environment and Human Factors (2017)Google Scholar
  16. 16.
    Kaur, P., Singh, G., Kaur, P.: Intellectual detection and validation of automated mammogram breast cancer images by multi-class svm using deep learning classification. Inform. Med. Unlock. 100151 (2019)Google Scholar
  17. 17.
    Kaymak, S., Helwan, A., Uzun, D.: Breast cancer image classification using artificial neural networks. Proc. Comput. Sci. 120, 126–131 (2017)CrossRefGoogle Scholar
  18. 18.
    Kowal, M., Filipczuk, P.: Nuclei segmentation for computer-aided diagnosis of breast cancer. Int. J. Appl. Math. Comput. Sci. 24(1), 19–31 (2014)MathSciNetCrossRefGoogle Scholar
  19. 19.
    Kumar, R., Srivastava, R., Srivastava, S.: Detection and classification of cancer from microscopic biopsy images using clinically significant and biologically interpretable features. J. Med. Eng. (2015)Google Scholar
  20. 20.
    Maitra, I.K., Bandyopadhyay, S.K.: Cad based method for detection of breast cancer (2018)Google Scholar
  21. 21.
    McDonald, E.S., Clark, A.S., Tchou, J., Zhang, P., Freedman, G.M.: Clinical diagnosis and management of breast cancer. J Nucl Med 57(Suppl 1), 9S–16S (2016)CrossRefGoogle Scholar
  22. 22.
    Ragab, D.A., Sharkas, M., Marshall, S., Ren, J.: Breast cancer detection using deep convolutional neural networks and support vector machines. PeerJ 7, e6201 (2019)CrossRefGoogle Scholar
  23. 23.
    Rajyalakshmi, U., Rao, S.K., Prasad, K.S.: Supervised classification of breast cancer malignancy using integrated modified marker controlled watershed approach, pp. 584–589 (2017)Google Scholar
  24. 24.
    Rao, S.K.: Integrated novel multi-phase level sets with modified marker controlled watershed for segmentation of breast cancer histopathological images (2017)Google Scholar
  25. 25.
    Saha, M., Mukherjee, R., Chakraborty, C.: Computer-aided diagnosis of breast cancer using cytological images: a systematic review. Tissue Cell 48(5), 461–474 (2016)CrossRefGoogle Scholar
  26. 26.
    Saraswathi, D., Srinivasan, E., Ranjitha, P.: Extreme learning machine for cancer classification in mammograms based on fractal and glcm features. Asian J. Appl. Sci. Technol. (AJAST) 1(4), 1–6 (2017)Google Scholar
  27. 27.
    Singh, A.K., Gupta, B.: A novel approach for breast cancer detection and segmentation in a mammogram. Proc. Comput. Sci. 54, 676–682 (2015)CrossRefGoogle Scholar
  28. 28.
    Uppada, R., Sanagapallela, K.R., Kodati, S.P.: Image automatic categorisation using selected features attained from integrated non-subsampled contourlet with multiphase level sets, pp. 67–75 (2019)Google Scholar
  29. 29.
    Wang, J., Yang, X., Cai, H., Tan, W., Jin, C., Li, L.: Discrimination of breast cancer with microcalcifications on mammography by deep learning. Sci. Rep. 6, 27,327 (2016)Google Scholar
  30. 30.
    Win, K.Y., Choomchuay, S., Hamamoto, K., Raveesunthornkiat, M., Rangsirattanakul, L., Pongsawat, S.: Computer aided diagnosis system for detection of cancer cells on cytological pleural effusion images. BioMed Res. Int. (2018)Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  • Vanaja Kandubothula
    • 1
  • Rajyalakshmi Uppada
    • 2
  • Durgesh Nandan
    • 2
    Email author
  1. 1.Department of ECEAditya Engineering CollegeSurampalemIndia
  2. 2.Accendere Knowledge Management Services Pvt. Ltd., CL Educate Ltd.New DelhiIndia

Personalised recommendations