Prediction of Cancer in situ using Machine Learning

Authors

  • Srikanth M S  Assistant Professor, Department of Information Science and Engineering, Nagarjuna College of Engineering and Technology, Bangalore, India
  • Dr. Jitendranath Mungara  Principal & Prof, Department of Computer Science and Engineering, Nagarjuna College of Engineering and Technology, Bangalore, India
  • Karuna S Kashyap  B.E, Student, Department of Information Science and Engineering, Nagarjuna College of Engineering and Technology, Bangalore, India
  • Kavyashree HM  B.E, Student, Department of Information Science and Engineering, Nagarjuna College of Engineering and Technology, Bangalore, India
  • Shrividya Bhatt S  B.E, Student, Department of Information Science and Engineering, Nagarjuna College of Engineering and Technology, Bangalore, India
  • Shubha G  B.E, Student, Department of Information Science and Engineering, Nagarjuna College of Engineering and Technology, Bangalore, India

Keywords:

Processor, Speed, RAM, Hard Disk, Graphics Card

Abstract

Breast Cancer is one of the cancers which can be common disease to women. This project is carried out by the algorithm CNN. This CNN is best way to predict the accuracy in faster way and also you can see the result in the objective requirements. It is also common disease in cancer. The design of breast cancer is based on breast dataset to collect and have efficiency of our database. The methods by achieving 95% and 99% accuracy.

References

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International Journal of Scientific Research in Science and Technology

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Published

2021-06-30

Issue

Volume 8, Issue 3, May-June-2021

Section

Research Articles

License

Copyright (c) IJSRST

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

[1]
Srikanth M S, Dr. Jitendranath Mungara, Karuna S Kashyap, Kavyashree HM, Shrividya Bhatt S, Shubha G "Prediction of Cancer in situ using Machine Learning" International Journal of Scientific Research in Science and Technology(IJSRST), Online ISSN : 2395-602X, Print ISSN : 2395-6011,Volume 8, Issue 3, pp.177-182, May-June-2021.