Compound Local Binary Pattern and Enhanced Jaya Optimized Extreme Learning Machine for Digital Mammogram Classification

Abstract

The fatality rate due to breast cancer still continues to remain high across the world and women are the frequent sufferers of this cancer. Mammography is one of the powerful imaging modalities to detect and diagnose cancer at its early stage effectively. A computer-aided diagnosis (CAD) system is a potential tool which analyses the mammographic images to reach a correct decision. The present work aims at developing a CAD framework which can classify the mammograms accurately. This work has primarily four stages. First, contrast limited adaptive histogram equalization (CLAHE) is used for pre-processing. Second, feature extraction is realized using compound local binary pattern (CLBP) followed by principal component analysis (PCA) for feature reduction. Finally, an enhanced Jaya-based extreme learning machine is utilized to classify the mammograms as normal or abnormal, and further, benign or malignant. The success rate in terms of classification accuracy achieves 100% and 99.48% for MIAS and DDSM datasets, respectively.