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casa 23(1):

Research Article

Breast Tumor Classification using Machine Learning

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  • @ARTICLE{10.4108/eetcasa.v9i1.3600,
        author={Salman Siddiqui and Mohd Usman Mallick and Ankur Varshney},
        title={Breast Tumor Classification using Machine Learning},
        journal={EAI Endorsed Transactions on Context-aware Systems and Applications},
        volume={9},
        number={1},
        publisher={EAI},
        journal_a={CASA},
        year={2023},
        month={8},
        keywords={Machine Learning, Tumor Classification, Accuracy, MCDM, Breast Cancer},
        doi={10.4108/eetcasa.v9i1.3600}
    }
    
  • Salman Siddiqui
    Mohd Usman Mallick
    Ankur Varshney
    Year: 2023
    Breast Tumor Classification using Machine Learning
    CASA
    EAI
    DOI: 10.4108/eetcasa.v9i1.3600
Salman Siddiqui1,*, Mohd Usman Mallick2, Ankur Varshney3
  • 1: Jamia Millia Islamia
  • 2: Hindu Rao Hospital
  • 3: Amdocs Development Center India
*Contact email: salman007.rec@gmail.com

Abstract

One of the most contagious illnesses and the second-leading cause of cancer-related death in women is breast cancer. Early detection of tumor is critical for providing healthcare providers with useful clinical information which can help them make a more accurate diagnosis. To accurately diagnose breast cancer, a computer-aided detection (CAD) system that employs machine learning is required. The paper proposes web based tumor prediction system which analyzes different machine learning algorithms for breast tumor classification to determine the best performing model. Different evaluation criteria namely accuracy, ROC AUC, etc are mostly employed for evaluating models but they make the selection of the best model strenuous. A multi-criteria decision making (MCDM) approach has been employed for selecting the best performing model.  Further, a web-based portal has been developed to provide the user interface for this functionality.  

Keywords
Machine Learning, Tumor Classification, Accuracy, MCDM, Breast Cancer
Received
2023-07-21
Accepted
2023-08-10
Published
2023-08-15
Publisher
EAI
http://dx.doi.org/10.4108/eetcasa.v9i1.3600

Copyright © 2023 S. A. Siddiqui et al., licensed to EAI. This is an open access article distributed under the terms of the Creative Commons Attribution license, which permits unlimited use, distribution and reproduction in any medium so long as the original work is properly cited.

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