Acta Scientific Applied Physics

Literature Review Volume 3 Issue 8

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Deep Learning Classifiers for Improving Breast Cancer Detection

Harshita Jain1*, Kiran Pachlasiya1, Ashish Chaure1 and Ritu Shrivastava2

1Assistant Professor, Department of CSE, SIRT, Bhopal, India
2Professor and Head, Department of CSE, SIRT, Bhopal, India

*Corresponding Author: Harshita Jain, Assistant Professor, Department of CSE, SIRT, Bhopal, India.

Received: July 10, 2023; Published: July 26, 2023

Abstract

Breast Cancer is largely deadly and not a homogeneous illness in current period that causes the passing of a tremendous number of ladies everywhere on the world. We developed a web-based application using HTML, Flask (Python) and Machine Learning to predict breast cancer outcomes using the parameters entered by the user. Our main objective is to eliminate the features of malignant breast growth cells and usual individual cells.

Keywords: Machine Learning; Python; Breast Cancer; Detection; Breast Cancer Detection

References

  1. “Detection Using Convolutional Neural Networks”. IEEE Journal of Biomedical and Health Informatics4 (2018): 1218-1226.
  2. R Platania., et al. “Automated breast cancer diagnosis using deep learning and region of interest detection (BC-DROID)”. in ACM-BCB (2017).
  3. Proceedings of the 8th ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics 17 (2017): 536-543.
  4. F Bray., et al. “Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries”. CA: A Cancer Journal for Clinicians 6 (2018): 394-424.
  5. AM Abdel-Zaher and A M Eldeib. “Breast cancer classification using deep belief networks”. Expert Systems with Applications 46 (2016): 139-144.
  6. D Lévy and A Jain. “Breast Mass Classification from Mammograms using Deep Convolutional Neural Networks”. (2016).

Citation

Citation: Harshita Jain., et al. “Deep Learning Classifiers for Improving Breast Cancer Detection". Acta Scientific Applied Physics 3.8 (2023): 08-11.

Copyright

Copyright: © 2023 Harshita Jain., et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.



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