A Hybrid Approach of Texture Feature and Gradient Orientation for Computer Aided Diagnosis System Based on Breast Density Classification

Abstract

A Computer-Aided Diagnosis (CAD) system can perform an accurate diagnosis and help radiologists by presenting a second opinion about breast density. However, the development of a robust CAD system for breast density classification is still an open problem. In this study, we proposed a CAD system based on hybrid intelligent machine learning technique for automatic classification of breast density on mammogram images. The proposed technique employs gradient orientation pattern HOG and texture descriptor CLBP-HF as features and K Nearest Neighbor (KNN) as classifier. The experiments were carried out on benchmarks public domain MIAS and DDSM datasets. The classification accuracy is 96.4% whereas recall and precision are 96.59 and 96.75% on MIAS dataset. Moreover, the comparison with the state-of-the-art breast density classification methods shows that the proposed method outperforms the existing methods on both MIAS and DDSM datasets, the improvement is significant on both datasets. The proposed method will help radiologists in assessing the breast density, which is important for breast cancer diagnosis.