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phat 22(30): e3

Research Article

A Robust Feature Extraction Technique for Breast Cancer Detection using Digital Mammograms based on Advanced GLCM Approach

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  • @ARTICLE{10.4108/eai.11-1-2022.172813,
        author={L Kanya Kumari and B Naga Jagadesh},
        title={A Robust Feature Extraction Technique for Breast Cancer Detection using Digital Mammograms based on Advanced GLCM Approach},
        journal={EAI Endorsed Transactions on Pervasive Health and Technology},
        volume={8},
        number={30},
        publisher={EAI},
        journal_a={PHAT},
        year={2022},
        month={1},
        keywords={Contrast Limited Adaptive Histogram Equalization, Advanced Gray-Level Co-occurrence Matrix, K-Nearest Neighbor, Artificial Neural Network, Random Forest and eXtreme Gradient Boosting},
        doi={10.4108/eai.11-1-2022.172813}
    }
    
  • L Kanya Kumari
    B Naga Jagadesh
    Year: 2022
    A Robust Feature Extraction Technique for Breast Cancer Detection using Digital Mammograms based on Advanced GLCM Approach
    PHAT
    EAI
    DOI: 10.4108/eai.11-1-2022.172813
L Kanya Kumari1,*, B Naga Jagadesh2
  • 1: Research Scholar, Department of Computer Science & Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram, Guntur District, Andhra Pradesh, India
  • 2: Professor, Department of Computer Science & Engineering, Srinivasa Institute of Engineering and Technology, Amalapuram, Andhra Pradesh, India
*Contact email: kanyabtech@yahoo.com

Abstract

INTRODUCTION: Breast cancer is the most hazardous disease among women worldwide. A simple, cost-effective, and efficient screening called mammographic imaging is used to find the breast abnormalities to detect breast cancer in the early stages so that the patient’s health can be improved.

OBJECTIVES: The main challenge is to extract the features by using a novel technique called Advanced Gray-Level Co-occurrence Matrix (AGLCM) from pre-processed images and to classify the images using machine learning algorithms.

METHODS: To achieve this, we proposed a four-step process: image acquisition, pre-processing, feature extraction, and classification. Initially, a pre-processing technique called Contrast Limited Advanced Histogram Equalization (CLAHE) is used to increase the contrast of images and the features are retrieved using AGLCM which extracts texture, intensity and shape-based features as these are important to identify the abnormality.

RESULTS: In our framework, a classifier called eXtreme Gradient Boosting (XGBoost) is applied on mammograms and the results are compared with other classifiers such as Random Forest (RF), K-Nearest Neighbor (KNN), Artificial Neural Networks (ANN), and Support Vector Machine (SVM). The experiments are done on the Mammographic Image Analysis Society (MIAS) dataset.

CONCLUSION: The outcome achieved with CLAHE+ AGLCM+ XGBoost classifier is better than the existing methods. In future, we experiment on large datasets and also concentrate on optimal features selection to increase the classification.

Keywords
Contrast Limited Adaptive Histogram Equalization, Advanced Gray-Level Co-occurrence Matrix, K-Nearest Neighbor, Artificial Neural Network, Random Forest and eXtreme Gradient Boosting
Received
2021-09-03
Accepted
2022-01-04
Published
2022-01-11
Publisher
EAI
http://dx.doi.org/10.4108/eai.11-1-2022.172813

Copyright © 2022 L Kanya Kumari 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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