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

L Kumari,B Jagadesh

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. OBJE

Highlights

Breast cancer is the most hazardous disease among women worldwide
The results show that Contrast Limited Advanced Histogram Equalization (CLAHE)+Advanced Gray-Level Cooccurrence Matrix (AGLCM) with XGBoost is superior to previous works done by other authors [12] [13] [16]
A pre-processing technique called CLAHE is applied to increase the contrast in mammograms

Summary

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 Cooccurrence Matrix (AGLCM) from pre-processed images and to classify the images using machine learning algorithms. 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. Several imaging modalities such as mammography, tomosynthesis, magnetic resonance, and ultra-sonography are used for BC detection Among these modalities, mammography is the best costeffective for detecting BC in the early stages [2]. It is a tough task to classify mammograms [4] These examine the breast to provide information like anatomy, morphology, contrast, etc. Detection of masses is difficult because they are very pronounced in density, size, shape, similarity to the healthy tissue, and image contrast [6]

Objectives

Results

Conclusion