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