Enhancing the Prediction of Breast Cancer Using Machine Learning and Deep Learning Techniques

M. Thangavel, Rahul Patnaik, Chandan Kumar Mishra, Smruti Ranjan Sahoo

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

With the surge of breast cancer, researchers have proposed many predicting methods and techniques. Currently, mammograms and analyzing the biopsy images are the two traditional methods used to detect the breast cancer. In this paper, the objective is to create a model that can classify or predict whether breast cancer is benign or malignant. Typically, a pathologist will take several days to analyze a biopsy, while the model can analyze thousands of biopsies in few seconds. For the numerical data, various machine learning classifications with supervised learning algorithms such as random forest (RF), K-nearest neighbor (KNN), Naïve Bayes, support vector machines (SVM), and decision trees (DT) are used. Then, deep learning—convolutional neural network is used to analyze the biopsy images from a dataset of images. An accurate result from the prediction are determined for saving the lives of people.

Original languageEnglish
Title of host publicationIntelligent and Cloud Computing - Proceedings of ICICC 2021
EditorsDebahuti Mishra, Rajkumar Buyya, Prasant Mohapatra, Srikanta Patnaik
PublisherSpringer Science and Business Media Deutschland GmbH
Pages581-593
Number of pages13
ISBN (Print)9789811698729
DOIs
Publication statusPublished - 2022
Externally publishedYes
Event2nd International Conference on Intelligent and Cloud Computing, ICICC 2021 - Bhubaneswar, India
Duration: Oct 22 2021Oct 23 2021

Publication series

NameSmart Innovation, Systems and Technologies
Volume286
ISSN (Print)2190-3018
ISSN (Electronic)2190-3026

Conference

Conference2nd International Conference on Intelligent and Cloud Computing, ICICC 2021
Country/TerritoryIndia
CityBhubaneswar
Period10/22/2110/23/21

Keywords

  • Breast cancer
  • Convolutional neural network
  • Deep learning
  • Image processing
  • Machine learning

ASJC Scopus subject areas

  • General Decision Sciences
  • General Computer Science

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