Classification of ultrasound breast cancer tumor images using neural learning and predicting the tumor growth rate

  • V. Mary Kiruba RaniEmail author
  • S. S. Dhenakaran


Ultrasound breast cancer tumor growth model is dependence on the cancer tumor growth size on time. Breast cancer tumor progresses on its growth and evaluated to estimate the survival time. The volume of the tumor changes because of the cell growth and loss. This research deals with the primary or an initial stage of breast cancer patients and screening with periodical time. Image processing techniques, specifying the systematic learning for tumor classification are the pre mechanism. Processing the tumor for high visibility, noises are removed and cancer, non-cancerous features are learned by the system through neural network back prorogation for automatic prediction. The screening reactivity in terms of lesion density affects the growth of tumor size. Patients who have effected by breast cancer, images are learned, and the tumor size is observed in the tumor growth model. The relationship between the historical and statistical growth rate defines that patient with; b + blood group around the age of 28 to 56 have a possible of 20% growth of malignant tumors than benign tumors.


Ultrasound images Breast cancer Tumor growth Image processing Age Blood group 



The authors gratefully acknowledge the funding agency, the University Grant Commission (UGC), Government of India, for providing financial support under the scheme of UGC-Maulana Azad National Fellowship (MANF). The authors like to thank with an Agarwal research center for translational research, Tirunelveli, for their kind help and thoughtful comments.


  1. 1.
    Abrahamsson L, Czene K, Hall P et al (2015) Breast cancer tumour growth modelling for studying the association of body size with tumour growth rate and symptomatic detection using case control data. Breast Cancer Res 17:116CrossRefGoogle Scholar
  2. 2.
    Anupriya K, Gayathri R, Balaanand M, Sivaparthipan (2018) Eshopping scam identification using machine learning. 2018 International Conference on Soft-computing and Network Security (ICSNS), Coimbatore, India: 1–7. doi:
  3. 3.
    BalaAnand M, Karthikeyan N, Karthik S (2018) Designing a framework for communal software: based on the assessment using relation modelling. Int J Parallel Prog.
  4. 4.
    BalaAnand M, Karthikeyan N, Karthick S, Sivaparthipan CB (2018) Demonetization: a visual exploration and pattern identification of people opinion on tweets. 2018 International Conference on Soft-computing and Network Security (ICSNS), Coimbatore, India: 1–7. doi:
  5. 5.
    BalaAnand M, Sankari S, Sowmipriya R, Sivaranjani S Identifying fake User’s in social networks using non verbal behavior. Int J Technol Eng Syst (IJTES) 7(2) :157–161Google Scholar
  6. 6.
    Benmazou S (2018) Wavelet based feature extraction method for breast Cancer diagnosis. IEEE Xplore Digital Library: 1–5Google Scholar
  7. 7.
    Helwan A, Abiyev RH (2015) ISIBC: an intelligent system for identification of breast Cancer. International Conference on Advances in Biomedical Engineering (ICABME) :17–20Google Scholar
  8. 8.
    Jiang J, Trundle P, Ren J (2010) Medical image analysis with artificial neural networks. Comput Med Imaging Graph 34:617–631CrossRefGoogle Scholar
  9. 9.
    Kaymak S, Helwan A, Uzun D (2017) Breast cancer image classification using artificial neural networks. Proc Comput Sci 120:126{131CrossRefGoogle Scholar
  10. 10.
    Liu Y, Sadowski SM, Weisbrod AB, Kebebew E, Summers RM, Yao J (2013) Multimodal image driven patient specific tumor growth modeling. In: Mori K, Sakuma I, Sato Y, Barillot C, Navab N (eds) Medical Image Com-puting and Computer-Assisted Intervention MICCAI 2013. MICCAI 2013. Lecture Notes in Computer Science, vol 8151. Springer, Berlin, HeidelbergGoogle Scholar
  11. 11.
    Maram B, Gnanasekar JM, Manogaran G et al (2018) Intelligent security algorithm for UNICODE data privacy and security in IOT. SOCA. CrossRefGoogle Scholar
  12. 12.
    Marcomini KD, Carneiro AAO, Schiabel H (2016) Application of artificial neural network models in segmentation and classification of nodules in breast ultrasound digital images. Int J Biomed Imag 2016:1–13CrossRefGoogle Scholar
  13. 13.
    Mehdy MM, Ng PY, Shair EF, Md Saleh NI, Gomes C (2017) Artificial neural networks in image processing for early detection of breast Cancer. Hindawi Comput Math Methods Med 2017:1–15CrossRefGoogle Scholar
  14. 14.
    Mohammed MA, Al-Khateeb B, Rashid AN, Ibrahim DA, Ghani MKA, Mostafa SA (2018) Neural network and multifractal dimension features for breast cancer classification from ultrasound images. Comput Elect Eng 70:1{12}CrossRefGoogle Scholar
  15. 15.
    Pan Q, Zhang Y, Chen D, Xu G (2017) Character-based convolutional grid neural network for breast Cancer classification. IEEE Xplore Digital Library: 41–48Google Scholar
  16. 16.
    Rani VMK, Dhenakran SS, Heber David A (2016) A mathematical modeling for quality based ultrasound breast Cancer image using color properties. Aust J Basic Appl Sci 10:44–51Google Scholar
  17. 17.
    Salguero AG, Capel MI, Tomeu AJ (2019) Parallel Cellular Automaton Tumor Growth Model. In: Fdez-Riverola F., Mohamad M., Rocha M., De Paz J., Gonzalez P. (eds) Practical Applications of Computational Biology and Bioinformatics, 12th International Conference. PACBB2018 2018. Advances in Intelligent Systems and Computing 803Google Scholar
  18. 18.
    Sivaparthipan CB, Karthikeyan N, Karthik S (2018) Designing statistical assessment healthcare information system for diabetics analysis using big data. Multimed Tools Appl.
  19. 19.
    Thein HTT, Tun KMM (2015) An approach for breast Cancer diagnosis classification using neural network. Adv Comput: Int J (ACIJ) 6:1–11Google Scholar
  20. 20.
    Vesal S, B NR, Davari A (2018) Classification of breast Cancer histology. Springer International Publishing 1Google Scholar
  21. 21.
    Xie X, Qu H, Liu G, Zhang M, Kurths J (2016) An efficient supervised training algorithm for multilayer spiking neural networks. Public Library of Science (PLOS) ONE 11Google Scholar

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© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of Computer ScienceAlagappa UniversityKaraikudiIndia

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