About | Contact Us | Register | Login
ProceedingsSeriesJournalsSearchEAI
phat 24(1):

Research Article

Impressive predictive model for Breast Cancer based on Machine Learning

Download95 downloads
Cite
BibTeX Plain Text
  • @ARTICLE{10.4108/eetpht.10.5246,
        author={Saravanakumar Selvaraj and Saravanan Thangavel and M Prabhakaran and T Sathish},
        title={Impressive predictive model for Breast Cancer based on Machine Learning},
        journal={EAI Endorsed Transactions on Pervasive Health and Technology},
        volume={10},
        number={1},
        publisher={EAI},
        journal_a={PHAT},
        year={2024},
        month={2},
        keywords={Breast Cancer, MRI Image, Classification, Human Intelligence, Segmentation},
        doi={10.4108/eetpht.10.5246}
    }
    
  • Saravanakumar Selvaraj
    Saravanan Thangavel
    M Prabhakaran
    T Sathish
    Year: 2024
    Impressive predictive model for Breast Cancer based on Machine Learning
    PHAT
    EAI
    DOI: 10.4108/eetpht.10.5246
Saravanakumar Selvaraj1,*, Saravanan Thangavel2, M Prabhakaran3, T Sathish4
  • 1: Jain University
  • 2: GITAM University
  • 3: Alliance University
  • 4: Mallareddy Institute of Engineering and Technology
*Contact email: saravanakumarme85@gmail.com

Abstract

INTRODUCTION: Breast cancer is a major health concern for women all over the world. OBJECTIVES: In order to reduce mortality rates and provide the most effective treatment, Histopathology image prognosis is essential. When a pathologist examines a biopsy specimen under a microscope, they are engaging in histopathology. The pathologist looks for the picture, determines its type, labels it, and assigns a grade. METHODS: Tissue architecture, cell distribution, and cellular form all play a role in determining whether a histopathological scan is benign or malignant. Manual picture classification is the slowest and most error-prone method. Automated diagnosis based on machine learning is necessary for early and precise diagnosis, but this challenge has prevented it from being addressed thus far. In this study, we apply curvelet transform to a picture that has been segmented using k-means clustering to isolate individual cell nuclei. RESULTS: We analysed data from the Wisconsin Diagnosis Breast Cancer database for this article in the context of similar studies in the literature. CONCLUSION: It is demonstrated that compared to another machine learning algorithm, the IICA-ANN IICA-KNN and IICA-SVM-KNN method using the logistic algorithm achieves 98.04% accuracy.

Keywords
Breast Cancer, MRI Image, Classification, Human Intelligence, Segmentation
Received
2023-12-06
Accepted
2024-02-22
Published
2024-02-29
Publisher
EAI
http://dx.doi.org/10.4108/eetpht.10.5246

Copyright © 2024 S. Selvaraj et al., licensed to EAI. This is an open access article distributed under the terms of the CC BY-NC-SA 4.0, which permits copying, redistributing, remixing, transformation, and building upon the material in any medium so long as the original work is properly cited.

EBSCOProQuestDBLPDOAJPortico
EAI Logo

About EAI

  • Who We Are
  • Leadership
  • Research Areas
  • Partners
  • Media Center

Community

  • Membership
  • Conference
  • Recognition
  • Sponsor Us

Publish with EAI

  • Publishing
  • Journals
  • Proceedings
  • Books
  • EUDL