phat 18: 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: Online First},
        volume={},
        number={},
        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.