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

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

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.

Keywords

Ultrasound images Breast cancer Tumor growth Image processing Age Blood group 

Notes

Acknowledgements

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.

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Copyright information

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

Authors and Affiliations

  1. 1.Department of Computer ScienceAlagappa UniversityKaraikudiIndia

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