An improved CAD framework for digital mammogram classification using compound local binary pattern and chaotic whale optimization-based kernel extreme learning machine

Abstract

The morbidity and mortality rate of breast cancer still continues to remain high among women across the world. This figure can be reduced if the cancer is identified at its early stage. A Computer-aided diagnosis (CAD) system is an efficient computerized tool used to analyze the mammograms for finding cancer in the breast and to reach a decision with maximum accuracy. The presented work aims at developing a CAD model which can classify the mammograms as normal or abnormal, and further, benign or malignant accurately. In the present model, CLAHE is used for image pre-processing, compound local binary pattern (CM-LBP) for feature extraction followed by principal component analysis (PCA) for feature reduction. Then, a chaotic whale optimization-based kernel extreme learning machine (CWO-KELM) is utilized to classify the mammograms as normal/abnormal and benign/malignant. The present model achieves the highest accuracy of 100% and 99.48% for MIAS and DDSM, respectively.